by Business Analysis,

There is no getting away from it – data continues to be one of the most important assets of a company. As such, it deserves just as much rigour and protection as any other organisational asset. The challenge is how do we manage it all and how can we safely use it to solve business problems? There are so many different types of initiatives related to data. Added to that, there are exponentially many more approaches and contexts in which to deliver them. In fact, it’s not an exaggeration to say that every change initiative an organisation undertakes will need and/or impact the organisation’s data in some way, shape or form.

This blog talks specifically about data analytics initiatives i.e. using data to derive insights and improve business decision-making and how one should start to tackle the problem to be able to better manage and utilise your data? A common misconception is that all you need is the data and technology…everything else will follow. This blog attempts to bust that myth and explain why this thinking is costly and unnecessarily risky.

It is said over and over again…

• “Our goal is to become a data driven organisation”,
• “Data analytics is critical to meet our future goals”,
• “Data is our biggest asset – we just need to centralise it”,
• “We have the data – let’s look at ways to monetise it”,
• “Implementing data will help us to derive actionable insights”,
• “We need better data governance to protect our customers and comply to regulations”

Data is viewed as a critical enabler of other strategic goals and permeates many executive conversations. Rightfully so. Protection of customer data is no longer a nice to have and data-driven insights are a key differentiator of whether an organisation thrives and becomes a market leader or is dogged with low performance and lack-lustre customer experience. Whilst better data management and utilisation is not a new goal, the success rate for organisations who get this right, remains painfully low.

There are many challenges to transforming an organisation to be a data-driven one. The biggest one is that most organisations are not starting out on a clean slate – they are inheriting years of poor data management and governance practices that has led to poor quality and/or siloed data. Over and above this there is a need to manage many other competing strategic goals and priorities. On the other side of the spectrum, is the new startup or new entrepreneur. Here the focus is on building the organisation’s brand and customer base – data is one of the last things that is considered until it is too late.

No matter where one starting from, there is an approach an organisation can follow to start to make their data work for them. Warning: the level of complexity and effort will vary depending on the specific needs and organisational context, but the foundation remains static and it is still possible to achieve this data outcome.

Before I explain the approach, it’s important to understand remind ourselves what the building blocks are for successfully utilising your data. Similar to building a home which is designed for energy efficiency, it’s easier when you have all the building blocks in place. The essential building blocks for a robust, data-driven organisation are:

• Data with a data architecture – this is the structured, comprehensive collection, storage and representation of all data that is important in an organisation. Enterprise data is typically centralised and supported with a design and definition of that data (metadata) to support the usage and data outcomes of the organisation.
• Data management – this is the set of end-to-end processes, definitions and specifications that are deployed in an organisation to manage data from creation to transformation to deletion.
• Data governance – this is the overarching framework which comprises policies, standards, controls and practices that are deployed in an organisation to ensure that data is managed and adheres to organisational and regulatory needs.
• People / consumers – these are the roles and/or forums with their respective responsibilities to participate in the end-to-end data management and governance processes and utilise the data. This includes all parties including external parties such as customers, regulators or business partners or even other systems.
• Presentation / access layer – this covers all aspects related to the accessibility, visualisation and utilisation / consumption of the data.

Each of these building blocks are equally important – together they create a continuous, sustainable loop starting from source systems to data consumers. They are framed by well- defined strategies and goals and aligned technologies. Each building block has to be enabled with specific capabilities so that the building block is whole and complete. Each block must deliver so that the “system” does not fail and continues to meet the organisation’s need. Ideally this data system should not only deliver the capability to a high degree of quality but also continuously improve and adapt itself to implement lessons learnt and to meet changing needs of the organisation. Additionally, the level of sophistication of each block can start from basics to very sophisticated – there is no universal right or wrong. It depends on what the organisation needs, the base it is starting from, and the right balance of keeping each building block fit for purpose. Focussing on just the data or just the underlying technologies (e.g. technical solutions for the data storage or the presentation / access layer) will create gaps in the full solution and increase the risk of failure.

This is where business analysis comes in. Exceptional business analysis will provide the discipline to help organisations bridge the gap between where they are now and where they want to be. Business analysis assists organisations to explore these different building blocks to get the right level of rigour and specification that is aligned to the business need. The “build it and they will come” or “it shouldn’t be too hard, we’ll figure it out as we go” approaches do not work or… they will…eventually, after significant trial and error, time and cost. The more complex the environment – the more problematic these approaches will be.

At BAPL we have a structured approach to help organisations to achieve their data outcomes. It starts with working with the organisation to confirm their data analytics goals and strategies. Being able to articulate and prioritise the data analytics vision, strategies and supporting objectives helps to set the scope for defining the current state and future state. We then partner with management, business data experts, technical and data specialists to define the current state and then the requirements for the future state. We ensure that the business objectives and expectations are translated into the underlying technologies and processes.

To become “data-driven”, an organisation requires trust and confidence in the data and the final solution – this is achieved by understanding all the fundamentals from the ground up.

Throughout the data analytics journey (whether using a waterfall, adaptive or a hybrid of the two), we maintain the traceability thread to ensure the “who”, “what”, “when”, “how” and “why” is continuously aligned. We develop an organisational knowledge base that can be leveraged by other business and change teams, who will also shape the data.

Our approach also incorporates understanding the organisational change management needs so as to work with the organisation to develop and execute a pragmatic implementation plan that ensures that each aspect of the data analytics solution is catered for. It is only through this discipline and complete business-data-tech alignment that success in data analytics initiatives can be achieved.