As digital transformation gains momentum across the globe, the conversation around data, its management, and strategies to guide its utilization is also evolving. By the end of 2019, digital transformation spends are expected to cross a staggering USD 1.7 trillion. A big part of this is investments in data technologies, such as Big Data, Data Lake infrastructures, and Advanced Analytics. In fact, a study by SAP and Oxford Economics revealed that 94% of business leaders are eager to spend more on data-related tools in a bid to accelerate the transformation journey.
So, what are the considerations one must look at before leaping on to the data bandwagon? With so much at stake and global data volumes steadily rising, organizations need a clearly outlined data strategy to unlock long-term benefits.
- Look beyond a one-size-fits-all model – Depending on the size of an organization and the nature of the business, data management strategies can vary significantly. For instance, sectors like healthcare and business financial services will have to factor in strict compliance while retail must regularly update data assets in line with changing customer sentiment. These critical factors should be identified before commencing strategy planning.
- Identify internal and external stakeholders – Data can come from various touchpoints of origination — both internal such as performance reports and audits as well as external, like customer feedback and market research. These trails must be clearly outlined with ownership allocated for each data type. This will ensure that the strategy finds its desired outcomes and datasets are always accurate, cleansed, and complete.
- Align IT understanding with business need – Expectedly, IT will be more concerned about structural, technical, and internal issues rather than a dataset’s real-world impacts. By making data strategy planning the sole responsibility of IT teams, organizations risk losing out on opportunities by allowing data to sit idle within the enterprise. For this reason, IT and business users must closely collaborate — from the grassroots level to the C-suite.
- Create a distinct mission statement – Demarcating the purpose of the data strategy can go a long way in ensuring positive results. Today, every enterprise has a sizable repository housed within their systems, and a statement mentioning the objectives behind its utilization is absolutely necessary. This will make sure that the strategy addresses all the stakeholders concerned, IT and business teams are on the same page, the needs of the organization are being met, and that there are no excessive spends.
- Assess existing capabilities – Evaluating where the organization currently stands on the data management maturity curve, will help to understand the distance left to cover. The mission statement will offer clear milestones and depending on the maturity status, low-hanging fruits can be targeted. Thereafter, any infrastructural gaps and areas of improvement can be bridged through intelligent investments.
- Ramp up Big Data capacities – Big Data offers the critical advantage of uncovering insights even from unstructured datasets. This means that organizations do not have to spend on converting, reformatting, and structuring information coming in from diverse areas. In this regard, capacities will include Data Lakes where Big Data can be stored and specialized analytics to churn out insights.
- Define governance and management duties – While data management is generally within the IT realm, governance must include intervention from the C-suite. Management refers to the entire lifecycle of a data asset from its creation to retirement. Governance, on the other hand, is far more holistic, linking data to use cases, determining application/retirement and ensuring privacy and compliance.
The technologies around data management are continually evolving; business leaders face a difficult choice when it comes to their selection, strategy, and implementation. To cut through the clutter in this dynamic environment and ensure effective decision making, it is vital to have a well-thought-out data strategy which covers every node of the organizational ecosystem.
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