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How to get a robust return on big data

It’s no longer just a trendy term, writes Prof. YUDHVIR SEETHARAM, head of analytics at FNB Business.

It’s no longer just a trendy term, writes Prof. YUDHVIR SEETHARAM, head of analytics at FNB Business.

In this digital age, big data is no longer just a trendy term. Businesses of all sizes are pouring resources into big data projects to make smarter decisions, deliver personalised customer experiences, and accelerate innovation.

Big data can help companies make sense of vast amounts of structured and unstructured data, enabling them to identify trends and patterns, predict future demands, enhance customer satisfaction and loyalty, and streamline operations to increase efficiency. However, this type of tangible and intangible return on big data investment isn’t simply guaranteed. Achieving meaningful ROI involves several key considerations:

Clear objectives – Set clear, S.M.A.R.T (Specific, Measurable, Achievable, Relevant, Time-bound) goals for your big data projects to keep the data analysis process focused. Whether you’re leveraging big data to enhance customer retention, optimise supply chain operations, or forecast market trends, having a clearly defined and measurable objective is essential. For example, if the objective is to improve customer retention, the goal could be to reduce customer churn by 10% over the next year by analysing data on customer behaviour and feedback to identify areas for improvement.

Yudhvir Seetharam, head of analytics at FNB Business.

Realistic expectations – Be realistic about the potential impact of big data investments, the timelines for delivering those impacts, and the scalability of your big data projects. It’s always a good idea to scale gradually, starting with pilot programs to test hypotheses and making the relevant adjustments before a full-scale rollout. For instance, before investing in a comprehensive big data analytics platform, start with a smaller project that addresses a specific business challenge and then scale up based on the results.

If you are a listed company, it’s very important to remember that even a significant investment in big data isn’t necessarily going to result in upward share price movement. There are too many other factors that influence share price to be able to categorically attribute an upward trend to big data. Similarly, a lack of upward price movement doesn’t automatically mean that your big data investment was wasted. It just means you need to find better ways of measuring the return on that investment.

A whole-organisation approach – Defining objectives and implementing big data strategies require input from all stakeholders. Everyone should understand the business’s goals with big data and their role in achieving those objectives. This includes not only the executives and data scientists but also the marketing, sales, and operations teams who will use the insights generated by big data analytics to make informed decisions.

A detailed implementation plan – Depending on your business size and budget, this may involve using existing talent, hiring new talent, or partnering with external service providers. Dedicated project managers and the right implementation culture are crucial, not just within the implementation team but across the entire organisation. For example, if the plan involves using existing talent, assess whether the current staff has the necessary skills and knowledge to implement the big data project or if additional training or hiring is needed.

A focus on sustainable results – Sustainability is crucial for business success and should be a fundamental aspect of big data initiatives. This involves leveraging big data to continuously understand the changing landscape and investing in capabilities to maximise the value from big data investments. For example, investing in advanced analytics tools and training employees on how to use them effectively can help ensure that the organisation can continue to generate insights and make data-driven decisions over the long term.

Appropriate success measurement – It’s vital to continuously evaluate the returns after implementing big data initiatives. ROI assessment should involve quantitative measures, like increased revenue, reduced operational costs, improved customer retention rates, and other aligned KPIs, as well as qualitative measures such as enhanced customer satisfaction, improved brand perception, or better strategic market positioning.

However, it’s important to remember that measuring ROI in big data can be challenging due to the interconnectedness of various factors affecting the bottom line, tangible and intangible cost factors, and the time required for operational efficiencies to permeate the organisation. It is also essential to isolate the big data investment impact from other ongoing customer experience enhancement investments and initiatives. For example, if the organisation has invested in both big data analytics and a new customer relationship management (CRM) system, you need to assess the impact of each investment separately as well as collectively.

Ultimately, an investment in big data offers immense potential for any business. However, to realise a significant ROI, it is crucial to strategically align that investment with the business objectives, invest in the right talent and tools, and continuously monitor the outcomes. By adhering to these best practices, organisations can harness the power of big data and ensure a rewarding ROI in the long term.

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