Bringing down the borders – a guide to monetising data

The amount of data being generated is breaking new ground, but how can businesses take advantage of this unparalleled insight?

Data is often seen as someone else’s problem, rather than everybody’s opportunity.

 

This is unsurprising, given the unimaginable scale of information generated by even modestly-sized businesses, and the perceived difficulty of turning this data into usable insight. The problem, however, is not one of data volumes or the complexity of analytical tools, but the lack of integration of different data sources and the technologies that can turn raw information into actionable insight.

If, as Clemenceau observed, war is too important to be left to the generals, then data is much too valuable to be left to the data analysts or data scientists. If businesses can unshackle their data and make it available to all, they can improve customer intimacy, transform their operations, unlock new revenue opportunities, and build new strategies informed by their vast stores of information.

Lakes and silos

Think of an established business in the same way as an old building. A handsome and sturdy construction might have served its original occupants well, but it presents severe drawbacks for modern business requirements such as open plan offices and Wi-Fi penetration.

It’s the same with our approach to harnessing the power of data. Legacy systems are usually not designed for managing, analysing and sharing information in a scalable and agile way. Common barriers to implementing analytics include lack of faster data ingestion and processing, multiple data silos, lack of self-service capability, and a deficit in the tools needed to master (and so monetise) big data.

The challenge facing most businesses is not with the size of the “data lakes” they deal with, but breaking down the boundaries between these separate stores of information. These barriers include multiple physical locations for data silos, complex IT infrastructure, multiple business processes, applications and workflows, enterprise policies and the separation between operational and analytical workloads.

As an example, a large logistics client had data sitting in silos across departments, LOBs and BU’s with a large amount of effort wasted in repeatedly solving the data preparation problem. Discussions were characterised by debates on correctness of numbers rather than defining and taking actions based on insights.

This complex web of entrenched ways of working and legacy systems presents a major barrier towards analysing, sharing and monetising data. The answer, however, isn’t to tear down the entire edifice and start again, but to knock down the internal wall that is preventing an enterprise from harnessing the power of analytics.

Breaking down the barriers

Things used to be so much simpler. Business units were discrete entities that only had to worry about gathering and managing their own data. The sales department looked at how many units it had moved, while finance crunched the revenue figures.

Today, valuable structured and unstructured data is generated from a significantly larger number of sources, from CRM systems to social media, point of sale to customer mobile devices, internet of things to marketing platforms.

All of these can provide information that will inform operations and strategies across every business department – but only if the raw data can be freed from the strictures of their individual silos, transmuted into a useable, contextualised format, and be viewed in real-time.

The solution is to build a single data lake which stores all the information generated and gathered by the enterprise. This lake acts like a reservoir from where information is channelled into the various analytics engines, business intelligence platforms, and visualisation tools that turn raw data into usable insights.

As well as creating a single “lake”, enterprises must also adopt master data management (MDM) practices to create a comprehensive view of data resources, and which can manage the complex task of gathering, cleansing, and analysing the information.

This also includes building taxonomies for different data domains and integrating with visualisation tools, as well as self-service portals so that individual employees are empowered to access the information they need.

By combining all of an organisation’s data in a single repository, enterprises can knock down the walls between teams and data silos, giving everyone access to the full range of information and insight they need to identify and exploit new opportunities.

Therefore, for this logistics client when we brought the data back into a cross-functional enterprise semantic layer with standardised business objects to create a boundary-less platform, client was able to create insights that were contextual, standardised and actionable leading to better conversion of revenue opportunity through commercial compliance as well as improvement in their operational efficiency.

From data to dividends

In the information age, data is power. Those who can gather more of it, analyse it more effectively and apply it faster will have a huge advantage over their competitors.

By ending the “siloisation” of data, organisations will vastly improve internal collaboration and information sharing between departments and applications. They will fully understand their own business and operations, be better equipped to exploit new market opportunities, bring new products and services to market much faster, develop a deeper understanding of their key relationships, and uncover correlations that will inform their future strategy.

Infosys worked with the same client to define and create a governed self-service platform leveraging a popular self-service visualisation tool on top of a data discovery platform to consume data from multiple transaction application and thus providing for true and structured democratisation of insights.

They were better able to predict demand and implement dynamic pricing, improve operations, identify up-sell / cross-sell opportunities, spot customer service or product problems, and harness contextual, behavioural or location data.

Above all, they will turn their internal and external data from a vast, untapped resource, into a source of new revenue opportunities that will repay the technological investment many times over through democratisation of insights by providing actionable information at all levels.

 

 

 

http://www.information-age.com/finding-value-localisation-big-data-123468880/



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