Making ensuring your data and analytics team is prepared for a potential economic downturn is more crucial than ever in a world when data team budgets are getting smaller and business expectations are rising. Here are the top ten things CIOs and CDOs should undertake to aid in recession-proofing their firms as teams race to adjust to the new normal.
1. Embrace intelligent automation.
Using RPA, AI, and ML approaches, smart data teams automate both technical and business procedures. We’ve discovered that employing an analytics operating system can greatly cut down on the amount of time that both technical and non-technical users spend conducting manual data work.
2. Use advanced analytics to give business stakeholders the ability to self-serve.
Technical counterparts of business teams who are given the freedom to self-serve are inherently happier. It is unreasonable to expect every business stakeholder to understand SQL, Python, or the sophisticated engineering software that technical teams utilise. Point-and-click interfaces made possible by no-code technologies can allow non-technical users to self-serve. Strong analytics operating systems can advance no-code by providing end-to-end capabilities for data collection, ETL, wrangling, data science, reporting, and other interactive web applications.
In addition to business stakeholders, technical data companies that no longer need to provide operational analytics across several cross-functional teams may also gain from increased self-service.
3. Consider tool bloat.
Many enterprise companies that have adopted a “modern data stack” are compelled to integrate a variety of tools from different parts of the data and analytics value chain. As a result, there is a huge increase in demand for integration engineers and potential cost growth. Try to identify tools that are integrated and offer capability that is more end-to-end in nature rather than using tools with overlapping capabilities. Tools that are integrated typically cost less overall and take less work to integrate.
4. Analyze where custom code is required.
Engineers almost always favor writing custom code to address issues. However, there are times when employing a third-party no-code technology might result in considerable efficiency gains and cost reductions. These technologies can frequently be used to carry out repetitive low-value tasks considerably more effectively than are required for internal tooling or operational analytics. Your bottom line will benefit from allowing engineers to work on more complex problems, and developer satisfaction may also rise.
5. Get ready for the worst-case situation of a labor cut.
Data and analytics leaders should be ready for a considerable reduction in team size given the current state of the economy. Even though we all hope these developments won’t occur, preparation pays off. With a considerably smaller crew, could you go on using your current business model? If the answer is no, think about alternative ways to view your company.
6. Prioritize and simplify reports that can add value to the company.
Each week, teams devote a substantial amount of analyst hours to creating reports for various business stakeholders. To save valuable time and money, prioritize the most important reports or automate your reporting procedures.
7. To prioritize initiatives, use data science.
I’ve discovered that the majority of businesses don’t use data science to make decisions. Finance, the supply chain, marketing, retail, and other areas of the firm can all use data science to find business drivers and opportunities even with financial constraints. When times are tight, you’ll be better able to eliminate lower-value activities if your team uses a data-driven technique for prioritizing the work that it does.
8. Think about switching to a data mesh from a data hub.
Data teams who try to centralize all data and analytics activity into a single, monolithic center frequently find themselves unable to keep up with demand and stifle the organization’s data-driven expansion. Business teams should be able to use their data to act autonomously and realize the potential with the aid of a more distributed data mesh architecture.
9. Work together with business users to eliminate shadow analytics work that is done manually.
Your company colleagues are putting in endless hours executing manual “shadow” analytics and reporting work, whether they are using Excel, PowerPoint, or desperate ad hoc tactics. Engage your business users to gain an understanding of their daily tasks and pain points so that your team is prepared to lead transformation if budgets are cut across the enterprise. It’s crucial to identify automation technologies that business stakeholders can use without having to learn how to code.
10. Look for ways to organize your data and analytics stack more effectively.
Consider using an analytics operating system to more efficiently coordinate your team’s work across existing tools if you do decide to keep a number of them in your data stack without having to put in a lot of engineering integration and maintenance work.
These ten suggestions will help you train your team so that you can manage any economic situation, whether it is increasing or declining. Agile, cooperative, and resourceful data companies will always be in high demand.
