Three Ways to Stick to Your Data Resolutions in 2022

Balaji Ramanujam, Head of Architecture, Data & Analytics at Infosys, highlights three effective ways to stick to your data resolutions for 2022 and make the most of new efforts in terms of performance, revenue, and experience.

Ring out the old, ring in the new!  

Ironically, this also seems to be how enterprises have been dealing with their data analytics strategy – readily abandoning the previous year’s unfulfilled agenda to adopt a new one in the new year. The core of every data strategy is mainly about three things – achieving targeted performance outcomes, increasing revenue, and enhancing customer experience. Doing the right things will help organizations achieve visible progress on these goals and ensure that they remain committed to their data strategies. Here, Balaji Ramanujam, Head of Architecture, Data & Analytics at Infosys, talks of the one thing enterprises should do in service of each of these goals – performance, revenue and experience – in 2022, so they can bring their data resolutions for the year to fruition. 

Leveraging External Data to Improve Performance

In the upheaval caused by Covid19, the importance of external data jumped manifold as everything from consumer behavior to supply chains got disrupted overnight. With past internal data and analytical models becoming less useful for predicting the future, organizations need to tap a variety of external information sources to feed new, AI-based predictive models. Better forecasts mean that enterprises are better prepared to meet uncertain, volatile conditions and are therefore better placed to achieve performance goals. Moreover, businesses that integrate massively underutilized external data into their operations stand to gain a significant advantage over their rivals. 

That said, enterprises need to approach external data thoughtfully, especially because it is so abundant and accessible. They should scan the landscape to discern the data types and sources best suited to their purpose and context. The data itself needs to be clean and clear of bias, inaccuracy, and risk of regulatory non-compliance. It is extremely critical to ensure that the outside data is trustworthy before using it. Also, using external data does not automatically guarantee outcomes. Anyone can access or buy the same data, but they should leverage it along with internal data as part of a centralized strategy to get results. 

See More: 6 Data Management Trends to Watch for in 2022

Making Internal and External Connections to Boost Revenue

Acquiring data is the easy part. But data in itself is worth little unless the enterprise leverages it to improve operations, make better decisions, grow the business, or monetize it into a new revenue stream. Research shows that not many companies extract full value – a 2019 survey of 200 European companies found that only 17 percent had set up a data monetization initiative, and 12 percent were in the process of piloting one. 

To earn value from data, enterprises should use it to resolve business challenges and optimize the business. For example, a global chemical company encouraged teams making good use of data to share their experiences across the organization and help other teams find the solutions best suited to their requirements. Over time, the company earned significant revenue from these initiatives. 

Enterprises can even use their data assets to create brand new revenue streams with the right approach. Companies should be able to differentiate their offering to customers who receive many such offers – often free of cost – on their smartphones. Enterprises should also modify their structures and processes, for example, break down silos to create a unified data monetization unit for the organization. Most importantly, they must understand that data monetization is an ecosystem game. It is about embracing a model where the enterprise acts as a data hub or platform. This model then allows the enterprise to include suppliers, partners and even competitors to take advantage of the platform for mutual benefit with the ecosystem participants sharing and maintaining internal, external and enterprise data with relevant context. The more the partners, the greater the revenue opportunity. Think of a connected car manufacturer. To exploit the various monetization opportunities before it, ranging from automated charging payments to targeted advertising to telematics insurance, the company must work with providers of charging infrastructure, payments, insurance, and media services, at the very least. 

See More: Overcome the Data Decision Gap With AI and Automation

Taking a Step Back to Enhance Experience

When trying to better their customer experience, most enterprises look at data very late in the funnel, at the point of transaction even. But fulfillment is only a small part of a customer journey that actually begins much earlier, just like the selling process, which commences long before a sale is finally consummated. Hence to truly impact experience/delivery, enterprises should work with data from everywhere in the value chain, not just at the last mile.  

Here, a journey map is useful for creating a detailed picture of customer experience from the point when the business first reached the user. By incorporating pre-engagement data into their analytics strategy, organizations can understand what a particular customer was thinking, feeling, and expecting before connecting with them. They can perceive the chronological sequence of events leading up to engagement – and what worked or what did not – to build a better experience. 

Most new-year resolutions begin on a high note before fizzling out. Unfortunately, it is the same with data resolutions. The three key practices mentioned above will improve data outcomes and motivate enterprises to stick to their strategies through 2022 and beyond.  

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