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Five Tips to transform your organisation’s analytics journey

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By Anshuman Bhar, CEO & founder at Aays Analytics

You’ve championed the art of Data Collection but the crucial question remains – is it helping the decision-making of your company. Converging from different sources in different formats for different users, organizations are awash with data nowadays. But when it comes to what to do with this extensive data, most top executives are clueless. It, thus, comes as no surprise that most companies are sitting on piles of data with not even a handful of them able to extract actionable information from hordes of bits and bytes.

Anshuman Bhar

To realize the maximum benefits from data, a holistic transformation in the organisation’s analytics journey is required.  This entails a complete overhaul in perspective toward digital transformation and to that end, the following five tips can be of immense help:

1) Cultural Reorganization: A combination of a compelling vision and well-documented strategic objectives of analytics programs can work wonders for the digital transformation of the company. The corporate strategies must include digital roadmaps and the creation of data champions and data analysts should be part of the strategies conceived at the business and functional levels of organisations. In addition, it’s important to have business partners at the core of analytics transformation programs so as to create a win-win situation for stakeholders across the value chain.

2) Long-Term commitment: The analytics transformation is a journey that requires a long-term commitment on the part of the organization. In their quest to become data-driven organizations, companies continue to evolve and mature in terms of their requirements for analytics tools and solutions. While some companies prefer investment in advanced data technologies and complex data architectures, others find it more appropriate to go for cloud-native cutting-edge solutions and engineering best practices. However, all these solutions might not be of much help for startups or business ventures which have just become operational through the route of bootstrapping. Therefore, don’t imitate a particular format and rather invest in accordance with the needs and requirements of your own organization. Also, as analytics transformation mandates a considerable investment and hence, it’s best to embark on the journey only when you have the required resources to take it to a logical conclusion.

3) Value Generation: Given the resource-intensive nature of Industrialising analytics, it’s absolutely essential for these solutions to deliver distinctive value that can help businesses to sustain a competitive edge over rivals. The unique nature of the generated value will further enthuse stakeholders to rally behind the analytics transformation and support the journey to the hilt. If analytics tools help organizations perform exceptionally well on expectation management, return on investment (ROI), and profitability, the journey of analytics transformation can be completed without any obstruction or resistance from stakeholders of the ecosystem.

4) Managing Talent Pool: Constantly upgrading technology and evolving paradigms have left the analytics segment severely short of skilled manpower. Companies are struggling to find experts and amidst sky-rocketing salaries of analysts, hiring new talent is proving extremely costly to companies. The situation is equally challenging for organizations having experts in-house as grooming and retaining the talent is increasingly becoming a tough nut to crack. In such a complex scenario, an organisation must choose carefully whether it wants to develop and retain the talent pool in-house or it would be better off leveraging partners’ capabilities for accomplishing these specialized tasks.

5) Data Fabric: To maximize benefits from the analytics transformation journey, the organisation must strive for adopting a holistic Data Fabric approach. By simplifying access to the data, this architectural approach encourages the consumption of data on a self-service basis. This also helps in getting rid of the data silos and frees data professionals from admin duties to spend their time productively on strategic analysis. Data Fabric is truly transformational in its capacity and can prove invaluable in deriving maximum value from the data collected from a range of resources, complex ERPs, and multiple systems among others.

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