Enterprises must prioritise security, governance and user adoption for successful implementation, writes Lee Wearne, Senior Business Intelligence Consultant at Insight Consulting:
The urgency of implementing enterprise data management strategies comes from an acute awareness that businesses need to put their data to work effectively to influence business decisions and efficiency, and ultimately help steer the direction of the business.
C-suites understand that this urgency is not a fad, that an effective enterprise data strategy is a fundamental component of their digital transformation journeys.
However, unless the data strategy is driven from the top of the organisation, there will – at some point – become a discrepancy between the business and data strategy. This disconnect can result in far more serious consequences than merely frustration – business outcomes as envisioned by the C-suites run the risk of becoming unattainable.
The data strategy must start at the executive level to ensure it aligns with the business outcomes. A data strategy being driven at lower levels in the organisation, separate from the C-suite which is designing the business strategy, is a recipe for an unsustainable disconnect.
The ideal state is where the lower levels in the organisation implement under the direction of the C-suite as this allows for the right mix of levers to be pulled for an effective implementation. The executives, being the drivers, will enable budgets to access new technologies, and – crucially – initiate effective change management. This is the only way to ensure buy-in across the organisation.
Effective change management also needs a top-down approach. It’s imperative for leaders to identify people in the organisation that have a natural knack for using data and then groom them to become champions for change. This approach, a business decision, encourages a culture of knowledge sharing and becomes exponential.
Obstacles to adoption of enterprise data strategies
Budgetary, infrastructure and skill constraints are global phenomena. Africa is no different, but in many cases some of these obstacles are more pronounced.
On this continent, infrastructure and data literacy continue to be the biggest hurdles. However, in both these instances there are reasons to be optimistic. Starlink, for example, being available in Nigeria and Mozambique, will no doubt enable far more adoption among enterprises to leverage their data more effectively.
Data literacy is also improving exponentially. Today we get requests from countries north of South Africa’s border for assistance in regression analysis and correlation coefficients. This didn’t happen five years ago.
Geography and industry also matters on the continent. For example, across regions and countries, the further one ventures from major centres, the less reliable – or even available – connectivity becomes, and this makes implementing a data strategy in an agricultural business, for example, more challenging. However, advances in technology are closing these gaps and driving increased adoption.
Corporate South Africa, despite the country’s energy challenges, has been more fortunate in that infrastructure has been more advanced and stable for a longer period of time. This has accelerated the widespread acceptance by enterprises that they absolutely have to invest in their data strategies to remain competitive and make accurate decisions.
Key components of an enterprise data strategy
Security, governance and enabling users are crucial components when implementing an enterprise data strategy.
Security
Discussions about security are usually centred on criminality – such as breaches or data theft. Of course these are vital, and necessitate effective defense mechanisms and backup and recovery. However, security is multidimensional and data integrity is a crucial component. An organisation that has poor data quality is going to make incorrect assumptions, and this is a business and security risk.
In addition to making sure data is protected, backed up and quickly available and ensuring data integrity for effective decision-making, there also needs to be sufficient investment in the actual infrastructure to manage dynamic loads necessitated by peaks in user activity, for example. Failing to do this can result in data loss, which is a security risk.
People, or users, are another important cog in the multi-dimensional discussion about security. Ensure the right people have the appropriate level of access to the right data. Beyond this, user education is paramount.
Governance
Good data governance should enable users to leverage their data in the most efficient way possible, while still ensuring integrity, security and appropriate accessibility. There needs to be a healthy balance between access control and ease of access and use. Beyond this, the right skill sets need access to varying degrees of actionable data insights to enable reports that directly influence business decisions.
User adoption
The key is effectiveness and ease of use. An enterprise with thousands of users needs a platform that is as intuitive and uncomplicated as possible.
There is little use in one department pulling data from a spreadsheet, and another using some or other BI interface. The data champions identified in the change management have a far easier time helping their colleagues when using a unified, intuitive platform.
Different levels of the organisation need different levels of insights. A platform like Qlik holds all data in its memory and enables this easy-to-use, single version of the truth across the organisation.
An executive may want a bird’s eye view of what’s happening, while another may request the “why” to drive important decisions. An intuitive platform like Qlik enables another user – with access to the same unified tool – to dig deeper and then easily drag and drop charts to explain and narrate a story about the business. The same principle holds true when leveraging the predictive power of Qlik – extracting insights for the business must be intuitive and simple.
About the writer:
Lee Wearne is a Senior Business Intelligence Consultant at Insight Consulting. He has 12 years of business intelligence related experience across multiple industries including medical, agriculture, finance, manufacturing, HR and commercial sales. A key part of Lee’s experience has been within the agriculture and manufacturing industry across several African countries at an enterprise level, namely Mozambique, Zambia, Tanzania, Malawi, Eswatini and South Africa.
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