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Data is the new currency. The Economist cites data as the most valuable resource ahead of oil. Companies across sectors and geographies are aggressively investing in technologies that provide critical insights about customers and their behaviours, improving existing services and making business decisions with better clarity. The competitive advantage of the power of data has indeed catapulted many companies into the next stage of digital maturity.
This large data repository is ineffectual unless companies learn to mine the necessary insights from it. A clear roadmap with a set of objective goals and metrics to track the progress towards digital transformation helps in maximising data assets and developing a structure that incentivizes collaboration between different data sources. Removing data dysfunctions, synchronizing data from different sources, letting go of siloed and deserted data are some of the ways to tackle big data problems.
Data analytics has travelled a long way from being descriptive and MIS-driven to intelligent, forward-looking analytics that has become a sine qua non for business-critical decisions.
Using data semantics helps in deriving business intelligence and improving engagement with a brand's end consumers. Moreover, an intelligent analytics approach requires a data lake architecture to access scalable data anywhere, anytime.
A well-defined strategy is key in translating large data into meaningful use-cases to understand customers. The approach should be to establish an end-to-end repository, including storing and managing data, integrating data silos, processing to improve data quality, applying business logic and deriving insights from data efficiently.
It is important to first identify and align the business objectives, by having wide-ranging discussions with key stakeholders, followed by identifying different data sources and mapping them with goals. This may involve integrating disparate data silos, synchronizing data coming in different formats, discarding redundant ones and building a focused data warehouse. Data available from this repository should be actionable and applicable for an enterprise-wide adoption which can be referred by different divisions and departments.
A dynamic big data strategy involves addressing infrastructural challenges, ensuring the right set of resources with appropriate skill sets with reskilling and hiring.
The technology landscape is fast evolving, and it is imperative for industries across all sectors to work on digital transformation actively to stay ahead of the curve.
Having a robust big data strategy and integrating it across all realms of the business end-to-end becomes visible as streamlined operations and policies in how enterprises deal with their customers and also ensures sustainable value creation for all stakeholder groups.
Big Data analytics is a keystone in establishing an organic approach in an ever-evolving feedback framework– improving productivity, efficient processes, value creation, better decision making, identifying new opportunities and target audiences, and ushering in an era of data-driven decision making.
Whether it is opportunity enhancement or risk mitigation, an optimal data strategy can help blossom an organisation’s business outlook in a competitive world.