Qlik Business Intelligence and Analytics

Qlik have been busy making a number of product enhancements and acquisitions lately to support Qlik's strategy of strenghthening and offering an end-to-end data management & Business Intelligence (BI) platform. Here we explain the value of these acquisitions and Qlik's move into Augmented Intelligence (AI) and Machine Learning (ML).

The Introduction of Insight Advisor - Qlik's AI Capability

Qlik is applying a unique approach to Artificial Intelligence (AI), which bridges AI and cognitive technologies with its UX and core Associative Engine to deliver what Qlik describes as Smart Start capabilities across the entire product portfolio. Smart Start features take advantage of the new cognitive insight engine in Qlik Sense®, which works with the Qlik Associative Engine to provide context-aware insights and automations across the analytics life-cycle. Taking this approach allows Qlik to combine machine intelligence with human intuition in new and powerful ways that unlock tremendous value for users and organisations.

According to Gartner, Inc., “A new paradigm — augmented analytics — is rapidly gaining traction. Central to this development is the use of ML automation/AI techniques to augment human intelligence and contextual awareness, and to transform data management, analytics and BI as well as many aspects of data science and ML/AI model development and consumption.”*

Insight Advisor debuted as part of the June 2018 Qlik Sense Enterprise release.  Insight Advisor auto-generates and suggests the best analytics and insights to explore based on the overall data set and a user’s search criteria, making insight suggestions increasingly relevant and valuable. This was further enhanced with the November 2018 release of Qlik Sense®, where Qlik introduced new machine learning capabilities into its cognitive engine and platform as part of Insight Advisor. Precedent-based machine learning allows the Qlik cognitive engine to get smarter over time, continually learning from user interaction and feedback as well as other sources. Qlik is the first analytics company to bring AI and ML capabilities together with human intuition in a way that truly augments the user’s power to discover. Users can directly train the machine by manually creating analytics, altering what the machine suggests, and providing direct feedback. The cognitive engine also learns from additional governed and trusted sources such as business rule definitions in global libraries and Qlik artifacts.

Because the associative engine is aware of a user’s context (selection state) at each step in the exploratory process and knows all the data that is associated and unrelated to that context, this is factored into machine driven analysis and insight suggestions to make them more relevant. It’s like giving the user “peripheral vision” that guides them to hidden insights and helps them see the previously unseen.

Watch Insight Advisor Video

Qlik's Move Into Conversational Analytics

A month ago, Qlik announced its acquisition of CrunchBot and Curnch Data to expand its augmented intelligence and cognitive capabilities in the area of conversational analytics.  CrunchBot works uniquely with Qlik As a Trusted Extension Developer (TED) accredited solution, validated by Qlik for quality and functionality and built upon Qlik’s open API framework and Associative Engine.  CrunchBot enables users to:

• Ask questions in a conversational manner through the Qlik Sense UI or popular collaboration tools such as Slack, Skype, Salesforce Chat and Microsoft Teams;
• Explore and ask questions of their data through voice interaction with integration to services such as Amazon Alexa;
• Receive robust answers and insights including additional context with auto-generated charts, interpretations, key drivers, period-over-period calculations and predictions for measures;
• Seamlessly dive directly into Qlik Sense analytics apps based on the results, with the context (selection state) retained;
• Leverage unique Natural Language Processing (NLP), which automatically trains itself and tracks user inquiries over time, and Natural Language Generation (NLG) which delivers insights for not only what is happening, but also why – and where to go next. 

CrunchBot is immediately available to customers through Qlik today, and will be licensed as a value-added solution to the Qlik Sense platform. To be named the Qlik Insight Bot, this new product will be available alongside Qlik’s existing Insight Advisor capability and will be further integrated with the Qlik cognitive engine to deliver value to its customers. Here's an article we wrote recently about Conversational Analytics.

Watch CrunchBot Video

Acquisitions For Improved Data Integration & Enterprise Data Management

Podium Data - aka Qlik Data Catalyst

Last year Qlik announced its acquisition of Podium Data, an enterprise-grade data management company whose solution simplifies and accelerates customers’ ability to manage, prepare and deliver analytics-ready data to every business user across a diverse data landscape. Enterprise data strategies have evolved to rely heavily on the creation of data lakes, however businesses are realising that these and other data sources aren’t designed to easily and quickly deliver data to the business user. In many instances, data lakes have only increased customer data complexity and management headaches. According to Gartner Inc., “Through 2018, 90% of deployed data lakes will be rendered useless, as they’re overwhelmed with information assets captured for uncertain use cases.” (Gartner. Derive Value From Data Lakes Using Analytics Design Patterns. 26 September 2017).

With Podium Data, Qlik provides customers with an expanding enterprise data management solution to transform their raw data into a governed, analytics-aware information resource. Qlik have re-branded this capability to be called Qlik Data Catalyst - a data management solution that simplifies & speeds up how you catalogue, manage, prepare & deliver trustworthy data. Qlik's Data Catalyst builds a secure, enterprise-scale repository of all the data your business has available for analytics, giving your data consumers a single, go-to catalogue to find, understand and gain insights from any underlying data source. The solution's data preparation and metadata tools streamline the transformation of raw data into analytics-ready assets, while the product's Smart Data Catalogue and graphical user interface help people easily discover and select whatever data they need. Built on a platform of hardened data security and featuring governance capabilities, you can easily integrate Qlik Data Catalyst with any of your other data management tools. Qlik Data Catalyst provides:

• Intelligent Data Profiling and Onboarding: Ability to profile and register data from any source or location throughout the organisation, providing a comprehensive understanding of every data element, with applied pattern matching, rules based metadata enrichment, and auto obfuscation rules to protect sensitive data.
• Automated Data Quality: Inspecting, improving and documenting the quality of incoming data through validation, formatting and encryption.
• Data Preparation and Publishing: Enriching and transforming data without additional programming, with the ability to publish data to downstream systems and be consumed by a broad base of users, including data scientists, analysts and business intelligence users.
• Smart Data Catalogue: A searchable data catalogue organised with tags, business definitions and data lineage that makes it fast and easy for business users to find, understand and “shop” for data.

Data Catalyst 4.0 released in January this year further broadens support for data sources beyond Hadoop to further unify enterprise data managemetn wherever data resides.  

Watch Qlik Data Catalyst Video

Attunity

Building on Qlik’s recent acquisition of Podium Data and the introduction of Qlik Data Catalyst, Qlik announced just a few days ago, its intention to acquire Attunity for its market leading data integration & big data management capabilities. Attunity provides cross-platform data streaming capabilities to support a shift to cloud and real-time analytics. This acquisition further differentiates Qlik by providing an expanded breadth of enterprise data management capabilities and will enable real time data delivery across complex cloud environments.

*Gartner: Augmented Analytics Is the Future of Data and Analytics, 31 October 2018 - ID G00375087

For some time there has been a shift in the way people interact with brands and devices, through a conversation. With messaging apps outpacing social networks in terms of monthly active users — and with the rise of wearable devices and the Internet Of Things (IoT) — businesses are following their audience to provide conversational experiences through chatbots and voice-enabled applications alike.

This shift isn’t just changing the game for how we use devices and connect to brands. It’s changing the way we look at data, too. Conversational analytics is quickly becoming a primary need for businesses who want to get to know their audience and provide a better user experience. Specific benefits of conversation analysis include sentiment analysis, enhanced social listening and greater personalisation to better meet users’ needs.

What Is Conversational Analytics or Data?

First, what is conversational data? It’s data that helps you make sense of how users are interacting with your chatbot or any device with a conversational interface. Conversational data is most useful because it shows individual users’ interactions in real time; web analytics, on the other hand, aggregates overall traffic data and is tough to act on while any individual user is browsing a site.

With conversational analytics, for example, you can single out a target demographic or user to see what they’re talking to a bot or application or both, then view every step of the conversation as it’s happening. While web or app analytics might show you a bounce rate, conversational analytics shows you exactly what was last said that makes a user leave a chatbot or voice application. By keeping record of each and every interaction on an individual level, this data is essential for retaining users, improving applications and understanding your audience.

“By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated”.1 

What can conversational analytics teach you about your audience? Quite a lot actually.  Data helps to segment your audience by identifying their interests and letting you act on it. A retail chatbot can learn what its users are most interested in — say, a 20-something young woman who lives in an urban environment and enjoys buying dresses. By assessing when she’s most active with the bot, the retailer can tailor to her tastes when she browses for a purchase. At other times, the retailer bot can push notifications related to her interests when she’s most likely to respond — in the same environment where she’s actively talking to her friends. With AI, this segmentation is automatic and dynamic in real time as the conversation evolves.

These metrics keep your users engaged and coming back for more. Using AI-based analytics, you can collate this information automatically, providing always-up-to-date reports on your audience activity in real time. Conversational UI is all about providing a personalised, custom experience, therefore, conversational analytics is rich in data that you need to know your users on an intimate, individual level.

Qlik's Move Into Conversational Analytics

Business intelligence leader, Qlik, just announced its acquisition of CrunchBot and Curnch Data to expand its augmented intelligence and cognitive capabilities in the area of conversational analytics.  CrunchBot works uniquely with Qlik As a Trusted Extension Developer (TED) accredited solution, validated by Qlik for quality and functionality and built upon Qlik’s open API framework and Associative Engine.  CrunchBot enables users to:

• Ask questions in a conversational manner through the Qlik Sense UI or popular collaboration tools such as Slack, Skype, Salesforce Chat and Microsoft Teams;
• Explore and ask questions of their data through voice interaction with integration to services such as Amazon Alexa;
• Receive robust answers and insights including additional context with auto-generated charts, interpretations, key drivers, period-over-period calculations and predictions for measures;
• Seamlessly dive directly into Qlik Sense analytics apps based on the results, with the context (selection state) retained;
• Leverage unique Natural Language Processing (NLP), which automatically trains itself and tracks user inquiries over time, and Natural Language Generation (NLG) which delivers insights for not only what is happening, but also why – and where to go next. 

This move further builds upon Qlik's augmented intelligence and machine learning capabilities for enterprises. CrunchBot is immediately available to customers through Qlik today, and will be licensed as a value-added solution to the Qlik Sense platform. To be named the Qlik Insight Bot, this new product will be available alongside Qlik’s existing Insight Advisor capability and will be further integrated with the Qlik cognitive engine to deliver value to its customers.

With the November 2018 release of Qlik Sense®, Qlik introduced new machine learning (ML) capabilities into its cognitive engine and platform as part of Insight Advisor. Precedent-based machine learning allows the Qlik cognitive engine to get smarter over time, continually learning from user interaction and feedback as well as other sources. Qlik is the first analytics company to bring AI and ML capabilities together with human intuition in a way that truly augments the user’s power to discover. Insight Advisor auto-generates and suggests the best analytics and insights to explore based on the overall data set and a user’s search criteria, making insight suggestions increasingly relevant and valuable as the machine learns from the user’s analytics interactions. Users can directly train the machine by manually creating analytics, altering what the machine suggests, and providing direct feedback. The cognitive engine also learns from additional governed and trusted sources such as business rule definitions in global libraries and Qlik artifacts.

Qlik’s AI and ML capabilities are unique because they work directly with Qlik’s associative engine, combining the power of AI with human intuition. Because the associative engine is aware of a user’s context (selection state) at each step in the exploratory process and knows all the data that is associated and unrelated to that context, this is factored into machine driven analysis and insight suggestions to make them more relevant. It’s like giving the user “peripheral vision” that guides them to hidden insights and helps them see the previously unseen.

1 Gartner: Augmented Analytics Is the Future of Data and Analytics, 31 October 2018

Just a quick update to share the news of advancements made to Qlik Sense with the September 2018 release.

Insight Advisor & Augmented Intelligence

Insight Advisor suggests the best analytics and insights to explore for users within a Qlik Sense application.  This level of augmented intelligence is extended in the Qlik Sense September 2018 release to all users of Qlik Sense Apps, including published apps in streams, to encourage further data exploration across the enterprise.

Advanced Authoring Capabilities

Building upon the Advanced Authoring capabilities delivered in June, this has been further advanced with the addition of high value features specific to building sophisticated data layouts, increasing the speed of application development and improving ease of use.  These include many QlikView like capabilities such as:

• Better control over information density within the Qlik Sense client for devices that support touch and mouse input
• Default bookmarks for setting initial selection state when applications are opened
• The ability to turn off responsiveness of the client for custom sheet sizing
• Expression Editor enhancements including direct links to help, restructured function categorisation and improved search capabilities
• Visualisation control improvements including conditional show/hide in pivot tables and customisable master measures

Improvements In The Qlik Management Console & Performance

The September release includes another customer requested, key enhancement: the ability to move multiple apps from stream to stream (removing any previous limitations from the Qlik Management Console.) Further, Qlik has made investments to improve performance and stability with upgrades to the open source front-end framework.

Enhanced Mapping

The September 2018 release will also deliver enhanced mapping capabilities, including a new multi-gradient Density Map Layer that is very useful when mapping data on a highly localised and detailed level. The Density Map Layer is a multi-colour gradient background where the colour intensity depends on the weight and closeness of points.  This map layer is useful for mapping data such as: crime statistics, house values, etc.  The release does not stop there with other general mapping improvements around additional controls and data inputs.

Connectivity

Connectivity has been continued with the addition of MS Azure DB and Jira connectors, while also delivering enhanced security with LDAP authentication to the integrated connector to Presto.

Enterprise Mobile Security

Mobile security requirements of large enterprise deployments are also addressed, delivering full support for AirWatch EMM, as AirWatch is among the fastest growing EMM vendors!

Strengthened Qlik NPrinting Reporting Capabilities

Qlik NPrinting is used for scheduled reporting.  Improvements have been made around administration, expanded authoring, and improved security for report consumption.  For example, you can now import users/roles from an LDAP source and, if desired, you can substitute the TLS Cipher suite.  In addition, Qlik NPrinting now has session expiration log out, output of reports in .xlsm formats, and report level password protection. 

To learn more about Qlik business analytics and how Qlik Sense may be applicable to your organisation, contact Inside Info.

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. 

Download ebook