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How to maximise insight from your customer analytics

It’s important to know how to successfully respond to consumer behaviour. And this relies on gaining insights from your customer analytics How to maximise insight from your customer analytics image

Speaking at the recent Big Data LDN conference, the senior technical product marketing manager at Qlik, Adam Mayer, addressed the problems many businesses have in gaining insights from analytics.

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‘Information drives value’ was the mantra Mayer led with, explaining that the valuation of goods providers such as Walmart, PetroChina and Procter & Gamble dropped in 2009. Ten years later, the value of more information-based companies such as Google, Microsoft and Apple have surged.

Adam Mayer’s presentation during Big Data LDN.

AI and analytics top the list of priorities for CIOs, with 81% telling Sapphire Ventures they are investing in this field. But for many, a lack of sufficient data literacy poses an obstacle. Data hoarding, which dictates a digital company’s processes, was also identified as a problem.

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Gaining insights from analytics

Qlik’s senior technical product marketing manager then provided three possible solutions to the general problem of gaining actionable insights from customer data via analytics:

1. Making use of DataOps

Putting forward Qlik’s data integration platform, Attunity, as an example. Mayer said that a DataOps approach to customer analytics would drive analytics-ready data, increasing productivity in the process.

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DataOps, according to Mayer, can also ensure that data is agile and that access to it is universal. It also ensures that the data can be trusted, which improves the problem of data silos and bottlenecks that emerge across multiple platforms.

This DataOps approach requires data streaming, the automation of data warehouse models, the generation of ETL scripts and the creation of data marts (that work independently from analytic solutions).

2. Enabling users with on-demand data

Going back to the idea of universally accessible data, it’s also vital that data can be accessed in real time, on-demand. Without this capability, tasks can’t be completed quickly.

This is where a product such as Qlik’s Data Catalyst comes in. As demonstrated at Big Data LDN, this program aims to automate and encompass three roles:

1. The data engineer, which addresses quality of data and what causes bad quality data;
2. The data steward, which addresses integrity and compliance of data, as well as undergoing checks on the treatment of sensitive data;
3. The data consumer, which searches for data within sets, e.g. a claims database, before publishing the data to the Qlik Sense app when it’s good enough.

Augmented analytics

Augmentation, when it comes to customer analytics, can help inspire a more data-driven and data literate workforce and allows for multiple delivery modes, as well as allowing for all data to be in one easily accessible place.

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Augmented analytics could also decrease the need to purchase multiple tools, making tasks less complex and reducing costs.

Mayer showed how Qlik’s Insight Bot can integrate with chat applications such as Slack to make queries about company analytics, such as sales figures, and get answers instantly. Data can be requested in the form of a variety of chart types, which can be saved with the aid of a ‘Favourite’ button.

Qlik Sense, meanwhile, is able to display analytics sheets and graphs that change in real time. Its Cognitive Engine is capable of surfacing insights that are based on customer data, as well as showing other related results that the user may be interested in.


3rd generation BI

Mayer went on to unveil his idea of 3rd generation business intelligence (BI) and how it can guide decision-makers when it comes to taking advantage of customer behaviour data.

Qlik’s 3rd generation BI involves the following:

1. The democratisation of data, making information catalogs accessible to all company employees and ensuring that the data is well governed.
2. Associative indexing and augmented intelligence (AI2): Meshing human intuition with machine learning in order to increase data literacy levels and trust.
3. Embedded analytics within all company practices from the edge to the core and the c-suite.


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