Data strategy: A must for companies
In the age of analytical competition, business models are gaining a competitive advantage (CA) and are directly related to organisations’ data strategy processes. Incorporating a data strategy enables the organisation to unlock the potential value of data analytics. Data strategy is defined as organising, governing, analysing, and deploying the information assets of an organisation. An organisation that makes extensive and systematic use of analyses to outperform its competition is defined as an analytical competitor.
The three levels of value creation from data: Data as a tool, data as an industry, and data as a strategy i.e development of organisations dedicated to building data resources that enable them to develop radically innovative business models. There is no one-sizefits-all approach when it comes to deciding on data strategy. Crafting data strategy is not a stand-alone activity. It goes back to the basics of first understanding; what are the key goals and objectives of the organisation and how to leverage data to make the organisation realise the objectives.
The planning for data strategy also includes key decisions like which data points are important for decision making, how data will be acquired, where it will be stored, who gets the access to data, and so on. Then comes the overarching decision of using tools, techniques, and analytical skills, which will help derive the insights from data. The decision on the analytical process also drills down to the fact - what are the core business needs of the organisation and how can data enable a more informed decision.
Incorporating the data strategy enables the organisation to unlock the potential value of data analytics which is often measured in terms of profitability, ownership, market growth, growth, innovation, leadership cost, product and service quality, delivery cycle time, customer satisfaction, flexibility and speed in meeting demands relating to the main competitors. There are six main areas where data can be put to work in any organisation.
These are decision making, understanding customers, and markets, launching smarter products, making intelligent services, improving business processes & operations, and monetising data. An organisation can choose to use one or all the six areas of using data to practice provided it is in line with the overall goal of the organisation. It’s important to start with identifying “why” an organisation needs to use data, exploring the wide range of possibilities of getting it done, analytical techniques to choose, and keeping in mind regulatory and ethical considerations. So, the organisation needs to hone its skills in developing data strategy and developing plans and processes geared toward realising organisational goals and objectives.
Data is an integral part of decision making; right from contact tracing to bringing back to work, predicting changes in demand to provide visibility into the supply chain. Innovations backed by Data, Analytics, and AI have become key to the success of organisations.
With the increased pace of Digital Transformation, companies have access to large data sets but do not completely understand how to unleash the power of data. Any organisation can continue doing blue-sky thinking to explore creative ways of utilising data, but it should lead with the right strategy. Trying to utilise data in a rush without knowing the result of burying heads in the sand and ignoring the datadriven outcomes in both situations will not lead to a positive result.
Trying to harness the power of data without a data strategy is like boiling the ocean. A data strategy encompasses what an organisation wants to do with data and decides top strategic priorities for data backed by key organisational objectives.
Conclusion: In the past, we tried to understand how the organisations analysed the available data by using descriptive analytics. Descriptive analytics helps the organisation to understand the past. With the availability of big data, we entered the area of predicting the future outcome. It uses different tools and techniques such as mathematical science, algorithm, machine learning, and computational modeling techniques. With the understanding of descriptive, predictive, and prescriptive analytics, businesses find better-informed decisions that take into account future outcomes.