Looking back over the past year many organisations have taken steps to harness the power of their data.
By leveraging data solutions to capture, store, analyse, organise and transform data, they’ve been able to develop sales and operations strategies against a challenging backdrop of remote work and coronavirus lockdowns.
However, it’s clear that for many organisations, the transformational power of data still feels out of reach and they’re struggling to monetise their data.
So, how can these businesses begin to harness the power of data in 2021 and use it to transform the way they work?
This article explores the nine things that all businesses considering data led transformation need to know.
Many businesses struggle with understanding the scale of what can be achieved through data-led transformation. Your approach needs to sell the benefits of data-led initiatives and ensure that the board understands the vision and tone around how the organisation will use data analytics.
The best performing organisations can 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 and good governance and start the process of building trust with those setting the strategy.
Data and analytics transformation is about focus and outcomes and the best outcomes are achieved when the organisation explores genuine business challenges, concerns and improvement opportunities.
Look at and identify areas that will have real impact with alleviating business challenges and where analytics can provide genuine and measurable value.
The starting point here is to undertake a discovery process around the business challenges you want to solve and then structuring it to put visibility on the data that’s available and how it can be used. This will help prioritise which projects represent the best outcomes for the business.
Merely viewing data on a dashboard may be helpful but transformational businesses extract value by linking the answers offered by your data, to actions, decisions and outcomes.
For example, knowing that you’ve 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 happened. This takes careful planning and a skilled data team working closely with management to frame unanswered questions appropriately.
Carefully executed smart collaborations and models for data lending and sharing you may facilitate greater data maturity and move your organisation closer to achieving 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 that data.
Internal collaboration is key and includes everything from data governance best practice to setting policies and appointing data stewards.
In organisations that have undergone data-led transformations, they’ve all recognised the importance of data teams, the IT team and other parts of the business coming together to identify the right team members to deliver the promised outcomes.
The main focus of data governance is and should be to protect data and adherence to regulations, privacy policies and broader legislation.
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.
Virtually all organisations that have succeeded in data-led transformation talk about the importance of strong data integrity and reliability.
At the point the data is selected for use in analytics or transformational activity, it’s important the data is verifiably of high quality to help build trust and comfort across the organisation so the data can carry the necessary weight to deliver the desired outcomes.
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 data-led 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. It allows organisations to start to explore ‘what if’ scenarios to support the delivery of informed decisions and transformational outcomes.
So, you’re adamant that the data in your organisation has value. Perhaps you’ve concluded 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, veracity, your plans and the alignment of your corporate and commercial strategies, all underpinned by your approach to managing technology.
It’s a lot, which is why it’s common for organisations to partner with data analytics solutions providers and use this to help accelerate their planning.
There’s no shortcut to achieving robust data maturity but follow the steps above and set a solid direction of travel and you’re likely to enhance the prospects of getting it right.
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. You can keep up to date with all trends in the data industry with Scotland’s data science and AI podcast, AI Right?.