Qlik Business Intelligence and Analytics

How Retailers Can Use Analytics & Personalisation To Satisfy Empowered Customers

Australian consumers are empowered consumers, and local retailers must satisfy them with personalisation to keep up.

Do you remember Coca Cola’s ‘Share a Coke’ campaign a few years ago? It featured personalised labels, so you could buy a bottle which – literally – had your name on it.  It was a massive success – in Australia in that first summer of its launch, Coke sold more than 250 million named bottles and cans in a nation of around 24 million people, then the campaign went global boosting sales in the US for the first time in over a decade. 

Although this is an early and relatively unsophisticated example of personalisation, it was highly rewarding for the brand.  Now, true personalisation using data analytics is the next big thing in Australian retail to satisfy empowered customers.  Emotional connections with consumers will become a point of competitive difference for retailers and engagement strategies that use business analytics smartly, will provide the foundation for growth.

The following are three ways in which you - using the power of data analytics - can start to leverage these personal connections using data you may already collect.

Tip 1: The Key To Personalised Shopping Is Learning Your Customer's Preferences Based On Their Behaviour

Adopting effective advanced business intelligence tools in order to accurately record and track over time customer purchase histories and business interactions is critical. Do people buy or browse certain goods more than others? Do they prefer to purchase items at certain times of the day or year, or more likely to purchase with other associated products or at certain price points? This is where advanced analytics comes in.

According to a report from Accenture in its Personalisation Pulse Check, 75% of consumers are more likely to buy from a retailer that recognises them by name, recommends options based on past purchases or knows their preferences.  It goes without saying that online retailers are well suited to offer personalised products and offers using data analytics and artificial intelligence, however this should be extended to include all retail channels.  Those physical retailers that also have an online presence and can integrate their data from all sources have a head start.

75% of consumers are more likely to buy from a retailer that recognises them by name, recommends options based on past purchases or knows their preferences.

Amazon Go takes this to a whole other level.  Retailers have been analysing market-basket purchases for many years now whilst continually trying to understand product shelf availability. But by taking advantage of IoT capability such as sensors, digital cameras and mobile technology, the industry is ready to uncover a much deeper level of insight.  Once you've got that data, we can work with Qlik's visual analytics platform to enable this IoT data to be associated with other traditional data sources, to help discover new insights into shopping behaviour. 

Brands today have a responsibility to make it easy for customers to engage, buy and consume what they want, how and when they want. The availability of data and digital technology today allows for a deeper level of personalisation needed to dynamically create experiences to each individual and context, across marketing, shopping, and services interactions. Yet many brands are still grappling with delivering upon customers’ desire for more personalised experiences. They create unintended barriers, such as too many options thrown at them or irrelevant recommendations.   In an era when your brand is the experience, it’s imperative that retailers deliver the ultimate user-friendly and tailored experiences or risk sacrificing sales and loyalty.

Tip 2: Create Personalised & Location-Based Offers

All too often, retailers are missing an opportunity by handing out blanket promotions.  Bringing together the online and offline environments as part of an integrated campaign can be highly successful. Using data from their online shopping, customers can be encouraged to visit the store with an email offering a relevant, personalised promotion. If a customer’s multi -channel buying history is available to stores at point of sale, then the retailer can see they are a loyal customer, even though it may be their first time in the store – and provide some kind of incentive such as a gift or discount.

There are a number of ways that traditional in-store retailers can respond to the increased demand by consumers for a more personalised approach, through the use of coupons or recommendations, for example. In an ideal situation, retailers should be able to rely on the power of real-time (weather, local events, competitor offers) at the point of sale to not only deliver customised offers, but do it both quickly and effectively.

Or related content based on items in the basket– ‘watch this YouTube video on how to pair the ingredients in your basket with wine’ could help enhance the customer experience. With the strong growth forecast by Planet Retail’s research in using click and collect services down the track from 35% to 76% of all shoppers - that is purchasing online and picking up in-store - it is important to ensure data is captured and analysed to understand behaviours exhibited through all interactions wherever they may be, so experiences can be integrated across the entire customer journey.

Tip 3: Build Better Customer Loyalty

The idea of personalisation remains key to future, sustainable success. Brand loyalty is largely an emotional matter. If a customer feels a retail brand understands their needs and lifestyle, they will remain loyal. It’s also a practical issue. Time-poor customers prefer to buy from a retailer they can rely upon, where they know they will find something they like and will suit them as individuals.

Many retailers are already on the road to customer engagement by creating tailored loyalty schemes and are able to gain data-yielded insights to boost the knowledge they have about their customers. This leads to better engagement, retention and long-term loyalty.  This is important, as Aberdeen Group’s Omni-Channel Customer Care research report suggests that brands with strong omni-channel engagement have an average retention rate of 89% versus 33% for brands with weak engagement.  That means businesses that excel in engaging customers across channels—including web, mobile, social media and in-store—retain more than twice as many customers as those without effective cross-channel customer care strategies. Key to this is regular training of customer service agents, managing miscommunication errors effectively and of course, effective management of customer data across channels. 

Again, it’s not that easy. With some loyalty schemes, it can take months before a retailer can actually build an accurate picture of the customer and be in a position to send tailored communications and offers. This lack of speed and agility can have a negative impact on overall engagement.

How To Adopt Data Analytics In Your Business

Inside Info has been working with retailers like APG for years in helping them easily consolidate data from multiple data sources (whether ERP, POS, CRM or warehouse systems), even coupling with contextual 3rd party data, to deliver dashboards and applications based on the leading Qlik business intelligence software platform that improve customer engagement, enable personalised customer experiences and communications and lift the bottom line.

When it comes down to it, personalisation comes back to improving the customer journey and improving their experience. It’s also a way for retailers to differentiate their service, reward loyal customers and build a more sustainable business.  To learn more about how Qlik business analytics can help and how it applies to retail you can download this new ebook Retail’s New Frontier - Visual Analytics. 

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Is Blockchain The Future Of Data Analytics In Australia?

Is blockchain the future of data analytics in Australia?

"Blockchain will be more disruptive than electricity."

This quote comes from Jeff Schumacher of BCG Digital Ventures, and sums up the current mood about this digital technology. Blockchain has the power to reshape civilisation, from how we exchange currency (where the tech originated, which we'll get to in a moment) to how we gather, protect and consume digital information.

Which leads us to our big question: How will blockchain revolutionise big data, if at all? We discuss some basics about blockchain in the Australian context - which was a key talking point at the recent CFO Edge event - and the ways we believe it will benefit business intelligence.

What Is Blockchain?

Blockchain is the underlying technology behind cryptocurrencies like Bitcoin and Ethereum.

It's like a digital ledger - information is written into a single "block", and when that block is finished, it moves into the "chain". The chain is accessible by anyone, anywhere, and every single access point holds the same information. Essentially, this makes it theoretically unhackable: If you try to modify a block in London, it won't match up with the same information stored in Sydney, New York or Shanghai, so the modification won't get into the chain.

To learn more, check out this animation from the World Economic Forum:

 

Blockchain In Australia

Australia is a nation leading the way in blockchain use, although the road ahead is still long.

The ASX, for example, is trialling the use of blockchain as a replacement for its existing CHESS system. The idea here is that by using digital ledger tech (DLT), there would be greater record keeping in the post-trade equities market, as well as faster transactions and better-quality data.

Treasurer Scott Morrison believes that blockchain could have "significant productivity, security and efficiency gains" for Australia, and has been championing blockchain research. Now a raft of organisations are involved with DLT in some capacity, including CSIRO, Westpac, CommBank, BHP Billiton and the World Wildlife Fund.


The Relationship Between Blockchain And Big Data

For big data to be effective, companies must gather vast quantities of information, store it, and be able to not only access, but also distill it into legible insights. While this is where Hadoop alongside software such as Qlik have greatly helped the industry, blockchain could improve on some of the same principles.

These are the benefits some analysts believe blockchain will provide to the big data sector:

Greater data integrity

Hadoop is already somewhat decentralised, but blockchain takes this to a much further extreme. Theoretically, if a multinational corporation used blockchain for its big data, it could store information from every location in servers around the globe, and no two servers would contradict each other. C-suite execs would have access to insights that span their entire global network, without concern that the data in their location was out of date compared to, say, a server in another country.

Easier to control

In the same way as the point above, a decentralised platform makes data easier to maintain and control. System admins in each location have access to a single node, but that node gives them visibility across the entire chain. If one system goes down or, in an extreme situation, it is compromised, the information cannot be modified because it would contradict the other nodes.

Easier to audit

Blocks are written into the chain in chronological order, and each action has a digital signature stamped into it. Theoretically, this would make the trail of information from source through to storage easy to track and audit, because a detailed history will always exist.

Examples Of Blockchain In Use

Let's look at how blockchain-based big data could help two very different Australian sectors: Fintech and healthcare.

Fintech

Because DLT records absolutely everything, financial analysts could potentially mine customer transactions in real time. They could learn spending habits in detail, or more importantly, track patterns using analytics and detect fraud as it occurs. This, of course, sparks a number of privacy concerns, but the industry is burgeoning and guidelines will no doubt evolve as does the tech.

Healthcare

Getting data wrong in healthcare could cost somebody their life. Blockchain can provide a number of data guarantees that doctors and healthcare administrators could use to better learn about their patients and, subsequently, provide better care. Customer records would be secure, highly accurate, and be accessible by multiple different providers - the latter helping calm the fear that important information would be lost in transit.

In Conclusion

Blockchain is not a magic solution for Australian businesses looking to invest in big data - not yet, anyway. There are still a number of privacy and security concerns, not to mention cost implications relating to a need for entirely new infrastructures, but it's all developing at a rapid rate.

For now, local businesses can turn to existing platforms to get fast, accurate insights on their big data. And that's where Inside Info comes in: We can provide some of the best business intelligence software in Australia, for mid-market and large enterprises. 

Gartner Recognises Qlik As An Analytics & BI Leader in 2018 Magic Quadrant

Gartner 2018 Analytics & Business Intelligence Magic Quadrant & Qlik a Leader

Gartner has just released its 2018 Analytics & Business Intelligence Magic Quadrant Report, and the Qlik business analytics platform has been named a Leader for the eighth consecutive year.   You can download a complimentary copy of the report here. https://www.insideinfo.com.au/qlik-business-intelligence-and-analytics/2018-gartner-analytics-business-intelligence-magic-quadrant According to Gartner in the report:

"Qlik's position in the Leaders quadrant is driven by progress on its roadmap for augmented analytics, improvements in marketing strategy, and ease of use." While Qlik's enterprise governance and Associative Engine for free-form analysis have continued to dominate as a differentiator.  

Different at the Core

Early on, Qlik set out to solve the biggest problem with modern BI tools – restricted linear exploration. Qlik’s unique associative technology brings all data together without complex data warehouses, and enables users to freely explore in any direction they want, leaving no data behind, and no path uncovered. The ability to combine all data sources quickly and easily, search and explore without limitation and pivot your line of thinking based on what you see is the core differentiator at the heart of Qlik’s leadership in the industry.

This free form exploration is valuable because insights come from truly understanding the data from all angles. To do so across an organisation, it is critical to elevate the level of data literacy of all users.

Over years of continued innovation and changing the face of BI, Qlik maintains its belief that all people need to drive analysis from any data source – on premise, in the cloud, in a hybrid environment, internal or external – without restriction or limitation.

Qlik continues to expand the market by delivering an extensible, cloud-ready platform that companies of all sizes can consider the centerpiece of their analytics strategy. Qlik Sense® is built on a fully integrated, cloud-ready platform powered by the patented Associative Indexing Engine. Qlik Sense combines enterprise readiness and governance with intuitive visualization and exploration, advanced analytics and self-service data preparation capabilities. This breadth and depth allows organisations to meet the broadest range of BI use cases from a single platform leading to consistent, data-driven decision making. With a flexible, low-cost monthly subscription, streamlined administration and a fully web-based experience, customers can benefit from Qlik Sense Cloud Business® with no capital costs or commitments. And through its open APIs, Qlik offers integration with best-in-class natural language generation and processing, advanced predictive analytics, and an immersive experience including augmented intelligence.

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Gartner Disclaimer

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

How Can An Australian Company Start Analysing Big Data?

How Can An Australian Company Start Analysing Big Data?

There is a lot of comment on Big Data and what it is, but according to the Telsyte Big Data market study, one in four Australian businesses lack a Big Data strategy as the main blocker for their IoT adoption, despite the fact 83% of these CIO’s plan to invest more in Big Data platforms. Here we’ll provide some useful tips on how an Australian company can start analysing Big Data to gain a competitive advantage using technologies such as the Qlik business intelligence (BI) software platform.  

Businesses are now able to capture, store, and process an incredible amount of data about almost everything. But a Big Data repository has no inherent value, and many companies that have invested heavily in Big Data technologies are still learning how to leverage it.

According to a recent Telsyte Australian Big Data & Analytics Market Study in 2017, more than half of Australian CIOs expect a fivefold or higher increase in the number of connected devices in their enterprise within the next five years, but one in four organisations note that a lack of a Big Data strategy is a blocker for their IoT adoption. Despite this, 83% of Australian CIO's plan to invest more on Big Data platforms.

83% of Australian CIO's plan to invest more on Big Data platforms.

A Quick Recap On What Big Data Is

Big data is the term that describes the large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis. Usually characterised by analysts around:

  • Volume - the amount of data to collect and store from varying sources such as business transactions, social media and information from sensor or machine-to-machine data, IoT.
  • Velocity - The speed at which data streams. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near real-time.
  • Variety - The different types of formats data comes in - from structured numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

Big Data flows can be highly inconsistent with periodic peaks which makes data loads challenging to manage, especially with unstructured data. The fact that data also comes from multiple sources, makes it difficult to link, match, cleanse and transform across systems. It’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly get out of control.

It’s important to remember that the primary value from Big Data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products, and services that emerge from analysis. The sweeping changes in Big Data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation.

According to the Telsyte study, 30% of Australian enterprises who are using or planning to use, Big Data, are looking at predictive analytics.  

What Type Of Big Data Applications Are Australian Businesses Using?

Telsyte found the intention to use Big Data analytics are high across a range of applications for Australian businesses, including financial modelling, customer interaction, security and fraud detection, retail sales and ecommerce, and IoT and machine to machine infrastructure.

Sales and marketing applications are one of the top three line of business use cases flagged by CIOs – one third of whom say they are looking to use Big Data analytics for this application.

However uptake is lagging for marketing teams, with just 15% of marketing departments having implemented Big Data analytics. So an opportunity exists here for the marketers among us to lead in this area as a means to differentiate. 

The benefits Australian CIO’s are seeking from their Big Data and analytics strategies are to improve productivity and decision making, with better product and service development now the number one business priority for Australian IT leaders.

However, the main barriers to adoption are factors such as software costs, lack of in-house skills and IT infrastructure requirements.

Companies like Woolworths are taking a big leap into the Big Data game, investing some years ago a quarter of a billion dollars into its Big Data endeavours in order to better analyse its consumers’ online and in-store spending habits. While Telstra uses Big Data to improve its marketing strategies and customer service by analysing massive amounts of consumer data in real-time. One of our long-standing clients, Qantas, has deployed Qlik Sense to reduce the impact of flight disruptions and maximise occupancy on planes by coupling data from its business applications with external weather and flight path data.

Where Do I Start?

The first question you need to ask before diving into Big Data analysis is what problem are you trying to solve? Having now looked at what others are doing, are you interested in predicting customer behaviour to prevent churn? Do you want to analyse the driving patterns of your customers for insurance premium purposes? Are you interested in looking at your system log data to ultimately predict when problems may occur? The kind of high level problem is going to drive the analytics you use. If you’re not sure, you can always look at what areas of the business that need improvement.

Once you’ve chosen one simple, well-defined issue, explore it and demonstrate a little value. Repeat those steps, over and over. Begin by taking a manageable sample of your data that’s suitable for the analysis you want to perform - this might prove difficult if your data is spread across disparate data sources, or might be in one database that requires complex queries to get the sample you’re after. You need to think about the data you need and how it needs to be organised. Identify any special requirements (such as legal) for handling the data and ensure compliance.

The sources for Big Data generally fall into one of three categories:

  • Streaming Data - this is data that reaches your IT systems from a web of connected devices. You can analyse this information as it arrives.
  • Social media data - this is data on social interactions, particularly relevant for marketing, sales and support functions. It’s often unstructured or semi structured so often poses a challenge.
  • Publicly available sources - Massive amounts of data are available through open data sources.

After you’ve identified the potential sources of data, you’ll need to consider how to store and manage that data, how much of it to analyse and how you’ll use the insights you uncover. Once you’ve got your sample you can perform data exploration, analysis and modelling. Try and keep your models simple to start with and rely on the same analytic methods you would use for any other data source.

Make Big Data Accessible To the Whole Business

Real value from Big Data is created when businesses can bring together data - big or traditional - from multiple sources or locations, and present that information in a way that encourages exploration and insight.

The ability to make Big Data accessible to the entire business drives value in two important ways. First, it means organisations are including Big Data sources into standard business analysis, thus gaining more detailed insight into key aspects of its operation. Second, it fosters a culture of inquiry in which experience and gut feel is supplemented with the power of Big Data. One of the significant issues expressed by IT leaders around the topic of Big Data is the struggle to find the ‘needle in the haystack’. By making Big Data accessible, more people will experiment with ideas around their data, eventually leading to greater business value.

Getting Insight Is As Much About Relating Data As It Is Collecting It

There are many tools focused on improving the ability for data scientists to perform analysis on massive amounts of data. But it’s important to go beyond the data scientist and empower all business users to perform analysis, regardless of technical skill. If we look at Qlik BI software as a case in point, it does this through:

  • A complete view of information - Qlik’s data integration tools bring together multiple disparate data sources to provide a comprehensive picture of the business. Qlik can connect to virtually any data source - including file-based sources like Excel and XML web content, application specific data like Salesforce, ERP etc and Big Data sources like Hadoop, Teradata and Cloudera.
  • Interactive, free-form exploration & analysis - Qlik’s patented, Associative Engine ensures that every piece of data is dynamically associated with every other piece of data, across all data sources. These associations can be extremely useful if there are hundreds or thousands of products, customers, geographies, etc. Such extremely large datasets can be sliced with a few clicks rather than scrolling through thousands of items. Context and relevance go hand in hand and quickly take what seemed to be a Big Data problem down to something that is quite manageable without any programming or advanced visualisation skills
  • Multiple methods to support Big Data - Because Big Data use cases and infrastructure differ in every organisation, Qlik offers multiple techniques - which can be used individually or in combination - to best meet your Big Data needs. Such as:
  • In-memory - Utilising the Qlik Indexing engine (QIX) optimisation of in-memory storage to compress data down to 10% of its original size.
  • Segmentation and chaining - Sectioning multifaceted data views into subject-specific views and then chaining these separate views with each other.
  • On Demand App Generation - Empowering the user to automatically create a purpose-built analysis app every time they select a slice of a very large data source.
  • Other methods - A robust set of Qlik APIs as well as a variety of partner technologies can be used for situations like a custom User Interface.

Australian businesses are gearing up to continue to invest in Big Data analytics as a source of differentiation, particularly to improve products and services, however the key is to bring together data - big or traditional - from multiple sources or locations, and present that information in a way that encourages exploration and insight. If you’d like to know more about how Inside Info and Qlik can help you with Big Data analytics, check out this ebook on 10 Ways To Transform Big Data Into Big Value.

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When to use data analytics: Are you in these situations?

When to use data analytics: Are you in these situations?

When should businesses use data analytics to inform strategic and operational decisions?  Here we’ll explore some common situations that businesses can face and discuss when to use data analytics or business intelligence (BI) tools like Qlik BI software, to make better use of the ever-increasing deluge of data at their disposal. Are you experiencing any of these situations?  If so then data analytics might just be the key, so read on:

  • Competition is heating up or you’re operating in a mature market.

  • It’s difficult to see a consolidated view of performance across different systems, entities or divisions.

  • You’re struggling to agree across the business on a single version of the truth when it comes to the numbers.

  • You need better visibility into the customer, sales, marketing, supply chain, operations and their financial impacts.

  • You understand “what” is happening in the business, but not “why” it’s happening.

Competition is heating up or you’re in a mature market

To succeed in highly competitive markets or a more mature market that are usually categorised by plateauing or declining growth rates, business analytics can be a valuable tool to direct where to differentiate your customer and/or service/product experience.  Using analytics to understand exactly what products or services are contributing the most to underlying profit, in addition to those segments of the market you’re focusing on and the resource effort to get there, can shed valuable light on where to focus attention that will generate the best return.  In the retail sector for example, using basket analysis can shed light on trending product combinations that you may not have thought of that could be wrapped up in a promotion in the form of a bundled offering to drive additional sales.  While detailed point and click financial analytics across operations can assist all industries looking for areas to remove wastage and find efficiencies to operate more effectively. In fact, Research by McKinsey in 2016 estimates that analytics tools have increased gross margins for manufacturers by up to 40%, with as much as 15% of after-sales costs also being reduced. We recently wrote an article about using analytics in the highly competitive media sector which may be worth a look.

Do you need a consolidated view of performance?

Businesses buy, sell and evolve over time.  This evolution often gives rise to inheriting multiple business systems that many not talk to each other, coupled with many manual processes that usually find their way in, to extract the information that senior management or business leaders need, to be able to perform at their best each day.  This is where a data analytics solution can help.  An effective business intelligence platform will automate the integration of data from the many siloed divisions or entities across one company or the Group, through inbuilt connectors, to then provide management with complete transparency across operations looking at whatever metrics are important to them (revenue, profit, inventory turns, capital etc).   

You can’t agree on a single version of the truth

In a business sense we often refer to a single version of the truth as having commonality in how  a certain metric is calculated in a consistent way.  For example, the Sales Director may measure revenue based on booked revenue, but the CFO may measure revenue based on when it’s received.  If there’s no consistency in how key metrics are measured then performance can be misconstrued across departments.  Data analytics software will keep all the data in a single repository, whether this be a data warehouse or a data store within the analytics platform itself, that has set rules around how metrics are measured and calculated, so there’s no ambiguity. Then dashboards or applications are built to provide visibility into a particular business process or area, like supply chain, and these dashboards all source the data from this singular data source, meaning everyone is working from the same page.

Want better visibility into customers, operations and their financial impacts

If you’re capturing data about customers, suppliers, from machines (plant equipment, counters etc) or from social media, then this big data can add valuable context when applied with other financial data to understand how your business is performing or could improve.  Customer analytics can marry data from social media to gauge the mood of customers, identify any problem areas or messaging that may need to change in campaign activity.  Data analytics can help identify exactly what products or sales reps are your best performers by region, division, brand or profit contribution. Another useful analytics application is the use of Pocket Margin Databases (PMDB) that allocate all costs back to an invoice line level to best understand profit.  Orora use PMDB’s extensively from the CFO to every sales rep to analyse product, plant and customer profitability.  Margin erosion items can quickly be identified as too can improvement opportunities that could otherwise go unnoticed.  You can read more about Orora and Qlik here.

You want to understand “why” something is happening

You know sales are down and you’re losing clients to your competitor but understanding why this is happening is the critical question to be able to answer.  This is where data analytics helps.  Dashboards that allow business leaders and teams the ability to freely interrogate the data through point and click analysis is usually better here to uncover insights. In our example, Qlik business intelligence (BI) software allows users the ability to cross filter the data by simply clicking in their dashboard on region, channel, time period and products to bring to your attention any anomalies or patterns in the data.  This is the “why”. 

There are many use cases for when businesses should turn to data analytics to improve decision making, these were just a few examples. We’ve been working with medium and large businesses since 2003 to help them make better use of their data to improve business performance. If you’d like to know more about how Inside Info can help, just reach out.

What Impact Is AI Having On Big Data Analytics In Australia?

What Impact is AI Having On Big Data Analytics In Australia

Australia’s leading companies need to have a better understanding of the rapid rise of Artificial Intelligence (AI) and what it means for their business. Over the last few years there has been a strong focus on big data, but there is not much awareness of AI among executive teams, apart from the general buzz. It’s important for CIO’s and other business leaders to understand the impact AI is having on big data analytics and their business as a whole, before they get left behind.   

Business intelligence (BI), also known as business analytics, is set to be transformed yet again as a result of big data and AI. As we continue into 2018, we’ll see the lines between business intelligence and artificial intelligence blurring even more. Thanks to the current capabilities of machine-learning systems that are capable of identifying patterns, habits and trends. Every board of a major ASX company is telling their CEO they’ve got to have a data analytics story and big data story, but what about an AI or machine learning story?

Many are already foreseeing big data to make a huge difference in the overall AI and machine learning landscape. In the annual Big Data Executive Survey conducted by NewVantage Partners this year, 88.5% of top executives surveyed were found saying that AI is going to be the most dominant factor that will have a disruptive impact on their companies.

Business Intelligence Evolves To Artificial Intelligence

To understand AI in ways that drive businesses, we must first start with something that business is familiar with - business intelligence or business analytics. BI software provides data and analysis to help business leaders make more informed decisions. The simplest BI systems are descriptive reporting engines that summarise business operations and tell you what has occurred. As data volume grows some BI software systems then incorporated predictive analytics.  This is using data to infer a likely outcome, such as forecasting the future trajectory of your data or the use of sentiment analysis - a kind of predictive analytics that analyses text data, such as social media conversations, and infers how the consumers feel about a product or a brand. Anyone who has ever shopped at Amazon or watched Netflix knows predictive analytics. These platforms recommend products or movies by optimising the similarity to our preferences. 

As BI applications and systems mature and become even more sophisticated they may also include prescriptive analytics, which not only forecasts potential future outcomes but prescribes specific sets or sequences of actions based on optimising some objectives. A common prescriptive analytics tool many of us use daily is a GPS, which prescribes routes to take us to our various destinations. This prescriptive analytics optimises an objective that measures the distance from your starting point to your destination, and prescribes the optimal route that has the shortest distance. 

Prescriptive analytics in advanced BI can recommend actions to optimise business processes, marketing effectiveness, ad targeting and many other business operations.  Regardless of what the analytics might suggest though, it is the human decision makers still who invariably make the final decisions on what to do. It’s artificial intelligence that then takes that next step.

Machine Intelligence Further Enhances Big Data Analytics

Big data analytics is not new in Australia. Companies such as Woolworths for example, invested millions into big data in a bid to better analyse the online and in-store spending habits of its consumers. Meanwhile, telecommunications giant Telstra has been building its analytics assets for over four years, and is using big data to drive improvements in its customer service among other things.

Today’s big data and parallel computing infrastructures which use graphics processing units (GPUs) have alleviated data and computing constraints around data volumes, processing power and the level of sophistication in predictive models. This has unleashed the creativity of data scientists and provided them with the freedom to use much more sophisticated models that form the basis of AI.

Simply put, the essence of AI is the automation of decisions from prescriptive analytics and the proper execution of all subsequent actions.

This requires AI to leverage real-time feedback loops from interconnected machines and databases that enable AI to learn from every experience and get smarter with every decision it makes.

Real-time feedback already exists in most prescriptive systems because it is closely related to the objectives that are being optimised. AI uses this constant stream of feedback data to feed its machine learning engine. This machine learning updates and improves our prescriptive analytics so the next prescribed decision is optimised even further to bring it closer to, or better than, what human experts would do. 

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through big data analysis, and engaging with customers and employees.  Taking a closer look at gaining insight through data analysis, progress in machine intelligence has had a number of impacts on big data analytics, in particular:

  • Fueling further enhancements in big data indexing

  • Big data analysis can provide machines with a more meaningful and contextually relevant idea of their functions. For example, the automation infrastructure of a clothing manufacturing plant based in Australia that exports its products to Europe will be able to judge market requirements for the coming winter season in a more accurate and insightful manner if it is able to access and analyse big data reports about the market, financial and weather conditions of that area throughout the year.

  • Driving the need for greater interconnectivity and integration between machines and other data

  • Expanding opportunities to build and embed smart capabilities into your applications with open API’s.  Intelligent assistants can be deployed to answer questions in a conversational format or facilitate real-time collaboration with immersive analytics

  • Opening up new opportunities for users to fully explore many big, complex and varied data sets in a number of different ways than ever before.  Analytics insights can be conveyed with new forms such as voice processing and Natural Language Generation (NLG) such as automating analysis like Qlik Sense Narratives that update and communicate insights on visualisations in seconds

The AI & Big Data Landscape For Australian Business

The consensus seems to be that AI-driven automation will spur productivity and prosperity globally among business. The reason is automation is primarily designed to drive human productivity and not human redundancy. A survey by Avanade found that 31% of organisations have already started using intelligent automation to break through the productivity plateau, with the number set to double by 2020.  Moreover, 86% of respondents believe they must deploy intelligent automation to be a leader in their field. The problem with Australia is that we are already a fair way behind the rest of the world.

The recent report, The Automation Advantage, commissioned by Google and conducted by AlphaBeta, found automation presents a $2.2 trillion opportunity for the Australian economy and could potentially create millions of jobs. The report found Australia lags behind the rest of the world with only 9% of listed companies making sustained investments in automation, compared with more than 20% in the United States and nearly 14% in leading automation nations globally.

According to the report, if Australia accelerated its automation uptake it would stand to gain up to another $1 trillion over the next 15 years.

With this in mind, rapid advances in artificial intelligence will result in fundamental changes across the business landscape within the next few years, according to technology experts. Powered by sophisticated algorithms and with the ability to learn over time, AI will be increasingly used in everything from accounting and legal analysis tools to heavy machinery and autonomous cars.

In Australia businesses have been looking at technologies to transform a financial services company’s credit risk modelling, or an oil and gas company’s insight into their data. While the accounting and legal sectors are two that will experience significant disruption from AI in the short to medium term. Many tasks that previously have been undertaken by junior staff will readily be handled by AI. For example, the task of trawling through large quantities of case law to determine the likelihood of a new case succeeding could traditionally have occupied a team of young lawyers for days or even weeks. Such a task could be completed by an IT tool in just minutes.

Similar efficiency benefits will also be seen within accounting firms where many low-level, repetitive tasks can be handled by software rather than humans.

According to predictions by research firm Gartner, AI technology will be embedded in virtually every new business software product by 2020. The firm says that, by that time, AI will have become a top-five investment priority for more than 30% of all corporate CIO’s.

The following survey by Deloitte published recently in Harvard Business Review on Artificial Intelligence and The Real World found that of those executives familiar with their companies’ use of cognitive AI technologies, more than half said their primary goal was to make existing products better, followed by optimising internal business operations and freeing up workers to be more creative in automating tasks, followed closely by making better decisions.

In fact, The Automation Advantage report mentioned earlier cites that AI could lead to an 11% fall in workplace injuries, a 20% rise in wages for workers who are redeployed to non-automatable tasks, and an increase in job satisfaction for 62% of low-skill workers as they focus on more creative and interpersonal activities. A retail worker might spend nine hours less on physical and routine tasks like stocking shelves and processing goods at the checkout and nine hours more on tasks like helping customers to find what they want and providing them with advice. Teachers might spend less time entering exam scores and more time with students.

Conclusion

The consensus is that AI driven automation will stimulate productivity delivering better interactions and decision making in a variety of areas for Australian businesses.  As we progress to a future where AI has the potential to significantly boost the Australian economy and affect many areas of our lives, including opening more opportunities for big data analysis, it’s no surprise that heated debates around employment, security, public safety, rights of robots, regulation, and social and ethical considerations, are now bubbling to the surface. 

BI Trends For 2018: The Secret To Success In Analytics

BI Trends for 2018 with Qlik

Usually at this time of the year we’re inundated with trends to account for the rapidly changing Business Intelligence (BI) landscape and what this means for organisations moving into the start of a new year.  Qlik’s global market intelligence team have revealed what they believe are key BI trends that businesses should take note of, particularly with an emphasis on the “de-silofication of data”.  Qlik have identified 11 emerging BI trends, but let’s take a look at a few of them that can make it possible for businesses to operate at the next level. But first...

What Is Data De-Silofication?

Many companies have found their own way of connecting data, people and ideas.  What sets them apart is how they take these fragments of systems and data out of their silos (department, an individual, a location etc) and connect this data quickly in a governed way, to use this information to fuel smarter business decisions. So that said what technology or behavioural changes are facilitating this.

Trend 1: Data Literacy Will Gain Company-Wide Priority

Analysts like Gartner predict that companies will and need to, take a more structured approach to increasing data literacy across the entire organisation.  No longer just a focus for insights, finance and IT teams.  Gartner in fact predict that “by 2020 80% of organisations initiate deliberate competency development in the field of data literacy, acknowledging their extreme deficiency.”  In a data literacy survey that Qlik did in September 2017, nearly 50% of workers are struggling to differentiate between what the data is telling them.  But there’s an appetite for employees to learn here, as 65% said they would be willing to invest more time and energy into improving their data skillset given the chance.

Nearly 50% of workers are struggling to differentiate between what the data is telling them. 

Trend 2: Data Gets Edgy

Due to the increased number of use cases of data, especially around IoT, offline mobile and immersive analytics, we’ll see a dramatic increase in organisations running workloads locally on a variety of devices instead of through public data centres.  Gartner believe that by 2022, as a result of digital business projects, 75% of enterprise-generated data will be created and processed outside the traditional centralised data centre  or cloud, an increase from less than 10% generated today.

Trend 3: New Ways Of Data Cataloging

In 2018, new ways of cataloguing data will be more deeply integrated with the data preparation and analysis experience.  This will help bring it to a broader audience that is able to easily combine governed corporate data, data lakes and external data as a service.

Trend 4: The Need For Interoperability

Companies are looking for fit-for-purpose software systems rather than a single-stack approach.  So in saying this, analytics platforms need to be open and interoperable, with extensibility, embeddability and with modern APIs.  This will shift analytics to become more embedded within workflows.

Trend 5: Blockchain Hype Will Drive Applications Beyond Cryptocurrencies

New techniques are emerging for processing, managing and integrating distributed data, making the location of data an increasingly smaller factor in information strategies.  This means ideas can be inspired by blockchain and peer-to-peer technologies. Initially connectivity to the blockchain ledger will have benefits.  But ultimately, the value might lie in the ability to verify lineage and authenticity of data using blockchain technology.

Trend 6: Analytics Becomes Conversational

The use of analytics has traditionally been focused on drag-and-drop style dashboard list boxes and/or visualization. While there continues to be value in this, there are new approaches available called “conversational analytics”, simplifying the analysis, findings and storytelling so that users more easily get to that one critical data point.  This can include natural language query, processing and generation augmented by search and voice. This can be helped through virtual assistants and chatbots through API integration, provide a new means of interaction.

Trend 7: Augmented Intelligence Changes Users Into Facilitators

In its current state, the most effective use of Artificial Intelligence (AI) is applying it to a diverse but specific set of problems. But in 2018 and beyond, blending AI with technologies such as intelligent agents, bots, and automated activities, along with traditional analytical tools such as data sets, visualisation, dashboards, and reports will make data more useful. That alone, however, isn’t enough. Instead, a system where machine intelligence and humans participate in a broader ecosystem, and the exchange and learnings that happen between them, is known as augmented intelligence.

With all these trends, governance, security & data qualityare becoming more crucial initiatives in an increasingly challenging environment. But to thrive in the analytics economy, organisations need novel ways of doing that while also addressing progressively distributed environments. Leveraging a truly open platform with an ecosystem harnessing the latest emerging trends, technologies, and methods will bring together data, people, and ideas. This will lead to more data literate users, innovation, and augmented intelligence — helping to successfully integrate data into our lives.

To see the full list of the top 11 BI Trends for 2018 identified by Qlik, download the ebook below.

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Forrester Wave Report: It’s Not Your Old School BI Landscape Anymore

Forrester rate Qlik a Leader in Business Analytics

The key message from the recent Forrester Wave Q3 2017 Report for Enterprise BI Platforms With Majority On-Premises Deployments, is that you better be evolving your business analytics to keep up, as it’s no longer an old-school BI landscape.  Qlik was rated a Leader in the report by Forrester.

BI technologies have evolved at lightning speed over the last two years which has forced many analysts to re-group their BI segments when assessing vendors and their capabilities. Enterprise versus self-service and agile BI are now one in the same, as BI tools must be enterprise class, quick to change and deliver and be user-driven otherwise they fail to meet customer requirements. In addition, cloud and data visualization capabilities are considered mandatory for a leading BI toolset and are no longer considered separate segments by Forrester. 

In the most recent Forrester Wave Q3 2017 Report for Enterprise BI Platforms With Majority On-Premises Deployments Forrester provides detailed product evaluations highlighting key BI product differentiators if you’re assessing capabilities for your organisation.

Qlik was named a Leader in the report, with Forrester stating that, "Qlik continues to differentiate with its powerful associative BI engine" with its exploratory UI noted as "one reason customer references awarded Qlik one of the highest scores for business value in terms of ROI."  Simply put, all BI tools work great when you know how to ask a question and what specific data sources, tables and columns contain the information you’re looking for.  What if you don’t?  This is the sweet spot for Qlik’s two products, Qlik Sense and QlikView according to Forrester.

To find out more, you can download a complimentary copy of the Forrester Wave research report below.

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Using Analytics To Prepare For The Sink-or-Swim Future of Australian Media

Using Qlik analytics to prepare for the sink-or-swim future of Australian media

Data analytics and better use of business intelligence (BI) software has become a crucial strategic exercise for all companies, however making good use of data has never been so important for the media and entertainment industry (M&E) as the rapidly changing digital environment continues to alter customer expectations.  And getting it right, literally, does pay. Research has shown that companies in general who have invested in data analytics are reporting an increased competitive advantage, with a stronger focus on specialised, innovative applications that have strategic benefits according to MIT Sloan Management Reviews publication, Analytics A Source of Business Innovation.

The very act of consuming content provides incredible amounts of data that can inform every aspect of content generation, packaging and distribution. The challenge in media, then, is not to generate data, but to integrate multiple data flows—new digital data along with more traditional sources of information—into their operations. Media companies must seize the opportunities this new data presents—or watch their pure digital competitors extend their lead in consumer intimacy.

Our work with media companies across the value chain, including content creators, aggregators and distributors, leaves us with no doubt that the opportunity to serve audiences better is immense, and the companies that are able to capitalise on this opportunity are likely to produce tremendous new value. Here’s some tips on how to prepare for the future of Australian media using analytics, to remain relevant.

Single View of the Customer Across All Platforms

Sounds easy right, however many struggle with this.  With all the variety and wealth of data available to media companies, a big challenge is how to bring all this data together to improve decision making.  This means utilising a variety of data sources from social media, data aggregators like Nielsen, advertising response data, traditional enterprise operating systems and other means. If you're an entertainment company that has a physical presence in theatres, theme parks, cruise lines or retail, you'll also need to analyse customer traffic, staffing, supply chain and logistical information to provide an optimal customer experience while increasing value and revenue from your properties.  This means providing a consolidated view of performance across all operations.  The inbuilt connectors available with BI platforms like QlikView and Qlik Sense for example, make it easy to draw data from these multiple data sources. 

Revenue Growth Through Better Targeting

As media and entertainment companies broaden their content delivery platforms and offerings, they must understand how customers interact with each to fully see customer value and opportunities to up-sell and cross-sell. Knowing how customers change their preferences for certain platforms over time is important. Such an understanding also enables setting of justifiable rates for advertising across platforms and pricing for individual and combinations of platforms. Marketing offers and campaigns can be more accurately targeted. The value of the customer to the company can grow, even if they lose interest in certain delivery platforms, content, or other offerings over time.

We work with many of Australia’s leading TV networks to help them improve targeting and revenue growth.  To achieve this, media companies need to understand customers better, how they consume content and services, and what advertisements attract follow-on activities.  Media and entertainment companies can better establish the value of their brands and offerings leading to increased revenue from advertisers. They can also offer services and subscriptions to a potentially valuable customer database for the advertisers to mine themselves.

Using Analytics To Predict Bankable Assets

Qlik analytics can be used to predict whether current trends will continue and any possible implications or opportunities. In media, value comes from understanding and predicting the content (movies, video, music, books and games) audiences want. Data and analytics firepower can increase a media company’s odds of getting it right.

We’ve all heard the story of when the producers of House of Cards were shopping the series to various distributors, Netflix and the other networks bidding for it all knew that political dramas, David Fincher films and Kevin Spacey in a sinister role were highly bankable properties. But Netflix brought superior data to the bidding, based on its in-depth and fine-grained analysis of viewers’ habits over many millions of viewings of shows. Netflix executives not only knew these qualities were likely to make the show popular; they also knew how long viewers had stuck with similar programs, through seasons and individual shows, and which characters had drawn the strongest interest. That confidence allowed Netflix to make a bolder bid and win the show as well as three Emmy awards, despite the recent decision to drop the series.  

Qlik Self-Serve Business Intelligence Drives Operational Efficiency

By providing self-serve business intelligence as a differentiator, media companies provide access to relevant information to everyone who needs it from the sophisticated needs of the pricing and insights teams to client information, discounts and placements for the sales rep, to be competitive.   If not, media companies will increasingly find themselves outpaced by the better-informed, quicker business moves of those that excel in analytics.

Inside Info & Qlik In The Media & Entertainment Industry

More than 300 media providers worldwide are using Qlik to overcome their business intelligence challenges and improve performance. Qlik's Data Discovery BI approach enables media companies to glean maximum insight from data by enabling easy, flexible analysis. Analysis can be performed in real-time keeping up with instant platforms like social media. Its intuitive dashboards easily shift from lag indicators to predictive analysis encouraging collaboration across the enterprise. If you’d like to know more about how Inside Info can help, just reach out to us, we’d love to hear from you.

Users Rank Qlik In BARC’s BI Survey 17 As A Leading Driver Of Business Value

BARC BI Survey 17 Qlik Highlights

The BI Survey 17, conducted by the Business Application Research Center (BARC), is the world’s largest independent survey of BI end users. This is the ninth year Qlik participated in the survey and the Company continued to have a strong showing of customer participation. Of the respondents, Qlik customers value the solutions’ flexible self-service capabilities for a broad range of BI use cases in a single platform leading to consistent, data-driven decision making.

Qlik Sense®, Qlik's next-generation application for self-service business intelligence (BI) and visual analytics, has ranked first in customer and product satisfaction, project success, business value and benefits, recommendation, embedded BI, and performance satisfaction among large international BI vendors in the industry’s largest independent global survey of BI users. Qlik Sense is a leader across 16 categories in the large international BI vendor peer group, including: cloud BI, self-service, vendor support, price-to-value, location intelligence, visual analysis, and innovation. QlikView®, Qlik’s proven, market-leading data discovery solution, is top ranked in the considered for purchase category across all peer group categories – and for the fourth straight year – in the large international BI vendor peer group.

“Qlik Sense is highly rated in terms of providing business benefits and customer and product satisfaction to customers compared to other large international BI vendors, making the vendor and its product set a leading driver of business value,” said Dr. Carsten Bange, founder and CEO of BARC. “The results of the large international vendors peer group show that Qlik Sense has convinced customers by yielding business benefits and delivering fast project implementation cycles. Our research shows that Qlik Sense customers give it very good marks in the large international BI vendors peer group in ‘Innovation’ categories such as cloud BI, mobile BI, and location intelligence.”

In addition to taking top honours among large international BI vendors, Qlik Sense was also recognised as a leader in 12 categories among the self-service reporting-focused products peer group, including: query performance, performance satisfaction, embedded BI, considered for purchase, and business value. Among the data discovery-focused products peer group, Qlik Sense was ranked first in query performance and hailed a leader in nine categories, including: business value, innovation, customer experience, project success, and visual design standards. QlikView also ranks first in the considered for purchase category in both the self-service reporting-focused products peer group and the visual-discovery focused products peer group.

The BI Survey 17 was conducted by BARC from February 2017 to June 2017. Altogether, 3,066 respondents worldwide answered a series of questions about their BI software. The survey offers a comparison of 42 leading business intelligence tools across 29 different key performance indicators including business value, customer satisfaction, customer experience and competitiveness. To download a complimentary copy of Qlik's highlights in the BARC BI Survey 17 report click below.

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