As the stock market has plummeted and inflation has reached a 40-year high, concerns about hard economic times and fears of a recession have changed decision-making within corporations. The focus has suddenly shifted to conservation and better utilization of resources, identification of efficiency drivers, and emphasis on profitable customers.
Data is a powerful tool that can help companies weather this storm and stay competitive during a challenging macroeconomic environment. Good quality data can provide detailed, granular insights into operating and financial performance metrics and point to areas of improvement that make the best use of limited resources. These insights are particularly useful during a downturn where companies place a premium on precise customer targeting, cost reductions, and operating efficiency.
However, leveraging the full power of data to meet these strategic goals requires a comprehensive and cohesive data strategy. Companies need to go beyond relying on limited internal data and blend it with appropriate streams of external data that capture the broader macro-economic environment. Such a blended approach will increase the range of information that is available for making better strategic decisions.
In order to pursue a blended approach companies need to make a strategic shift in their resource allocation from data science and analytics to data engineering. The difference in the results that such a shift can help achieve is remarkable. A powerful team of data scientists can only help extract the best possible insights from the limited amount of available data through better models. However, a powerful data engineering team can substantially enhance the amount of high quality data that are available by blending internal and external data. The combination can substantially increase the canvas that the data scientists can use to build models and derive insights.
However, companies have to be prudent during tough economic times while building their data teams and have a clear focus on the return on investment on their efforts. Data scientists have been in high demand recently and the field has been identified as one of the most promising career paths. However, data engineers are also expensive and in short supply. Therefore, the investment in building the capabilities to identify, acquire, and ingest high quality external data and blend it with internal data to drive decision-making is likely to be large.
A much better option to achieve the same goal is to collaborate with an external data engineering service, such as Spectre, that can provide a lower cost and scalable solution for the problem. These partnerships offer several advantages. First, because these services operate at a larger scale than any individual company does, they offer a lower cost option relative to an internal team. Second, these services are more up-to-date with the latest technologies and tools and can string them together to achieve higher levels of efficiency. Finally, unlike an internal data engineering team, they do not require a fixed investment and can help optimize headcount and reduce costs by 30-50%.
Overall, a robust data strategy to survive and thrive in a tough macro-economic environment rests on three pillars:
1. Leveraging external data and blending it with internal data to achieve superior insights
2. Shifting the focus from data science to data engineering to build a high quality data portfolio
3. Partnering with a data engineering service to optimize headcount, reduce cost, and keep the data pipeline refreshed
An effective data strategy can empower an organization to take advantage of a continuously changing environment and gain a competitive edge over its less sophisticated rivals.