Data-Maturity-Guide

Improve Data Maturity in 9 Steps

Looking back over the past year it is obvious that many organisations have taken a number of steps forward to harness the power of their data. Leveraging data solutions to capture, store, analyse, organise and transform their data for many in 2018 has become de rigueur, but if we explore the data maturity of many of these organisations, it becomes clear very quickly that there is still much more to be done before they reach a transformational level of maturity.

In our experience, the organisations achieving a high-level and transformational stage of data maturity are those with a narrow focus on insight and data for revenue generation. At the top of the organisation, they will explain that Big Data is a huge driver for organisational change and that they have embarked upon implementation of specific applications that will allow them to monetise their data.  Many proudly show their roadmap for migrating away from traditional approaches such as an enterprise data warehouse towards a leading-edge Big Data Lake. Many have one or even multiple platforms across their enterprise and have moved towards the provision of self-serve and secure access to their applications both internally and externally.

So… what is behind such approaches and how can others ramp their data maturity and realise similar benefits?

 

  1. Data Leadership isn’t just approving the budget!

Tone at the top is crucial for all strategic data initiatives. But the problem here is that for many boards and leadership teams, there is simply not enough of an understanding about what can really be achieved.  Whether by design or through a weak approach, the board is often poorly informed or mis sold the benefits of expensive initiatives without being instrumental in setting the vision and the tone around the organisation’s use of data analytics.

In our experience the best performing organisations are able to demonstrate an effective ‘bridge’ between the data teams and the organisation’s strategy.  This takes skill in many areas.  An effective data function with solid governance and an eye on trust, privacy and security matched with a properly informed and educated leadership team. This ‘bridge’ can interact with various departments and executives to socialise the advantages of analytics initiatives, good governance, and start the process of building trust with those setting the strategy.

 

  1. Explore real business problems.

Data and analytics maturity is about focus and outcomes. Just because data is available it doesn’t mean you should always implement a big data driven approach.  In our experience, the best outcomes are achieved when the organisation explores genuine business challenges, concerns and improvement opportunities. We advise you look at and identify areas likely to have real impact with business challenges where analytics can provide genuine and measurable value. The starting point here is to undertake basic discovery around a range of business problems that you are seeking to resolve.  Structure the business problems to put real visibility into the availability of data and an appreciation for how it might be used.  Use this to help prioritise where next and which projects represent the best outcomes for the business.

By way of an example, exploring the reasons behind a decline in user signups or transactions might seem like it lacks granularity, but it is important to categorise all reasons of such decline to narrow down available options to exploit data in crafting a solution and answer otherwise unanswered questions.

 

  1. Link decisions and outcomes to analytics.

Merely viewing data on a dashboard may be helpful but transformational businesses extract value here by linking the answers offered by your data, to actions, decisions and outcomes. As such, knowing that you have experienced a downturn in transactions may have some limited value, the real goal here should be to understand the reasons why a downturn or decline in transactions has been experienced.  This takes careful planning and a skilled data team working closely with the management to frame unanswered questions appropriately.

 

  1. Data lending and smart collaboration

Relying entirely in your own data limits the potential value available.  With carefully executed smart collaborations and a model for data lending and sharing you may facilitate greater maturity and move your organisation closer towards the ability to achieve transformational outcomes.

However, think carefully here before diving straight in.  Trust, privacy and security must remain a central tenet to the way you work such relationships and carefully defined and delineated data will be the best way to proceed and maximise the maturity of your use of such data.

 

  1. Good governance is helpful but great governance is better!

Internal collaboration is key here.  In those organisations in which we have observed transformational data maturity, they have all recognised the importance of the data teams, the IT team and the business coming together and identifying the right team members to deliver the promised outcomes whilst respecting best practices for data governance. It includes everything right from setting policies to appointing data stewards. The main focus of data governance is and should be to protect data and adherence to regulations, privacy policies and broader legislation.

 

  1. Integration is key.

Good data strategies that the team at the top can invest in and the data team can deliver are reliant on the ability of the organisation to manage and integrate data and relevant technology components.

We observe transformational data maturity in organisations that consider new tools for data integration at an early stage of their plan to fully exploit available data. However, this necessitates knowing your data intimately: its sources, its veracity, its probity and its accuracy.  Organisations achieving best value from their data recognise that their data is often not standardised or effectively classified. Data intended to be used may not even exist in the organisation yet and, even if it exists, it might be in locked in hidden systems or localised fiefdoms.

What this means is that effort must be applied to understanding where data may be squirreled away, its classification, use and security regime.  Ultimately it will be necessary to explore methods by which such data can be appropriately integrated. You should also start working towards identifying and overcoming political and emotional issues of opening up data to other parts of the organisation. This, unfortunately, is often the biggest challenge in improving data maturity.

 

  1. Data quality.

Virtually all organisations in which we have witnessed high data maturity have cited the imperative for strong data integrity and reliability. When dealing with a large quantum of data it is perhaps satisfactory to accept some quality issues in the data.  However, at the point the data is selected for use in an analytics or big data activity, it is imperative that the validity and source of the data are determined so that comfort can be established that the insights delivered will carry the weight necessary to deliver desired answers and outcomes.

 

  1. Beyond dashboards and towards predictions.

The leading edge or perhaps even the bleeding edge is not a state of business maturity that suits all.  However, with improved data maturity and when well on the way towards transformational data maturity, it is feasible for many organisations to start to open the door to advanced analytics solutions.

Available to organisations that have mastered much of the above is the genuine prospect that they can make use of advanced analytics for more complex business challenges. Predictive analytics have proved to be the destination for many such organisations. In short, a predictive data model is a comprehensive data solution involving algorithms and techniques, which can be used for both structured and unstructured data sets to determine future state outcomes. As such, organisations can genuinely start to explore ‘what if’ scenarios to support the delivery of informed decisions and transformational outcomes.

 

  1. Direction of travel.

So… you are adamant that the data in your organisation has value.  Perhaps you have come to the conclusion that a careful collaboration wrapped with trust, privacy and security, will enable you to achieve transformational outcomes?  This all needs to be wrangled carefully.  It’s not just about the data, its governance, its quality, probity and veracity – it’s also about your plans and the alignment of your corporate and commercial strategies, underpinned by your approach to managing technology.  It is common for organisations to partner with data analytics solution providers and use this to help accelerate their planning. In our experience there is no shortcut to achieving robust data maturity. However, follow the steps above and set a solid direction of travel and you’re likely to enhance the prospects of getting it right.

 

Summary

So… there you have it… the basics of kickstarting your journey to achieving a transformational data maturity or at the very least stepping up to extracting greater value from your data.  Want some help benchmarking your current state or help with understanding what steps to take?  Contact Purple. 020 3376 7447

Read more of our advice on big data and other innovative business concepts at Purple’s Innovation lab:

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