5 Ways to Adapt Your Analytics Strategy to the New Normal

Covid 19 has upended all traditional business models and made years of carefully curated data and forecasting practically irrelevant. With the world on its head, consumers can’t be expected to behave the same way they did 9 months ago, and we’ve witnessed major shifts in how and where people and businesses are spending their money. This new normal— the “novel economy,” as many have dubbed it—requires business leaders to think on their feet and adjust course quickly while managing the economic impact of lockdowns, consumer fear, and continual uncertainty. The decisions they make today will affect their company’s trajectory for years to come, so it is more important than ever to be empowered to make informed business decisions.

In recent years, organizations across industries have started to implement advanced analytics programs at a record pace, drawn by the allure of increased efficiency and earnings. According to McKinsey, these technologies are expected to offer between $9.5 and $15.4 trillion in annual economic value when properly implemented. However, most organizations struggle to overcome cultural and organizational hurdles, such as adopting agile delivery methods or strong data practices. In other words, adopting advanced analytics programs is happening across the board, but successful implementation takes a long time.

And to be frank— in this unpredictable new business environment, time is a luxury that leaders do not have. To keep up with the changing tides in consumer demand, supply chains, and more, you need an advanced analytics platform that can help you stay one step ahead. Your organization needs to redefine what it means to be agile and implement new technologies faster than ever.

Here are 5 steps you can take to leverage analytics to help your business thrive in the novel economy:

Step 1: Center Analytics Around Business Priorities

Aligning your analytics approach with your business priorities is arguably the most critical step in deriving meaningful insights and seeing ROI from your analytics investments. This seems like an obvious statement, but a 2019 study showed that only 30% of companies align their analytics strategy with their broader corporate strategy.

In the near term, you need to build new data streams that will report on business-critical issues so that you can make decisions quickly and effectively as market tides constantly shift in response to the pandemic. This will also set you up for developing long-term strategies based on the entirely new data and models of the post-pandemic world.

Step 2: Embrace Cross-Functional Teams

The key to an agile, flexible, and adaptable response to today's ever-changing business environment is communication. Siloed teams are a thing of the past; keeping precious business data locked up in sales, marketing, or ops can at best slow the pace of innovation and at worst cause you to miss out on lucrative opportunities.

Interdisciplinary collaboration — where business, operational, and analytics experts work side by side— brings a diversity of perspectives and ensures that your analytics strategy is aligned with broader organizational priorities. To understand the macroeconomic impact of Covid-19 on your industry, you need to bring data together from across your organization. Once you start opening the communication lines, you might be amazed at the synergistic momentum you’ll find. Your marketing team might be sitting on critical market research data that can inform product development, or your supply chain team could help forecast disruptions in key materials that require a shift in strategy for a product roll-out.

Step 3: Deploy Agile Strategies

In recent years there has been a major push towards adopting agile practices, and there are mountains of resources explaining the massive organizational shifts required to do so. But in the reaction to Covid 19, the world has realized something pleasantly surprising: most organizations are more agile than they think. Agile methodology can be taught in long and drawn out ways. Still, in a crisis, it appears that teams naturally migrate towards agile practices to stay flexible and respond to business needs at the moment.

Agile methodology can be deployed in simple and surprisingly effective ways. Iterative development sprints represent one agile method that can help teams test ideas rapidly, gain useful insights faster, and be empowered to make informed decisions quickly enough to improve business outcomes. While we all navigate an unpredictable market that is ever-changing, agile delivery methods can help your organization create a minimum viable product in weeks, freeing up precious resources and enabling you to stay ahead of the curve.

Step 4: Trust Data to Inform Decisions

It is broadly accepted that data is a powerful tool in decision making, yet many executives are hesitant to trust data over their gut instincts. However, “intuitive” decisions based on knowledge of business operations before Covid are almost certain to miss the mark in the new normal. To succeed with your data strategy during and after the pandemic, it is essential to put aside any antiquated thinking and focus on the picture your data is painting for you.

And in that vein, now is the time to put the power of decision making in the hands of workers on the front line who have the most up-to-date and intimate understanding of what your data is trying to tell you. This concept is counter to most traditional top-down decision-making protocols, and therefore can be a tough transition for some executives. However, if you can have a little faith in your workforce, surprising things can happen.

Step 5: Brace for Data Challenges

Covid-19 has upended most data models, and you might find that in the short term, your forecasting tools are churning out more errors than relevant information. Even the trustiest and most well-honed data models are likely to struggle with a large amount of data drift due to pandemic-related causes. Take the time to conduct a data and model audit to identify the risk of model drift and errors in existing models in operational, financial, and risk areas. These audits can be patterned after traditional model-validation efforts typically conducted for regulatory purposes.