Businesses must use data to drive exponential growth

Business growth is essential to its survival, but I’m smashing doors by saying this.

Growing a business is essential to its survival, but I’m opening doors by saying this. On the other hand, do all business leaders understand that their growth depends on better management of their data? I’m not sure! Over the past decade, companies in every industry, from pharmaceuticals to financial technology, have embraced big data. Data science has shifted from being an esoteric academic discipline to a key role for organizations according to the World Economic Forum. Its importance will only increase over time. It is worth remembering that growth at scale is only possible for organizations that know how to manage their data. In fact, it has become unreasonable these days to have to wait for a monthly or quarterly report. Data must be quickly available to help make strategic decisions on the spot.

Obviously, many organizations have had a hard time moving their data to the cloud, but once achieved, they benefit from better management of their data. Some of them seek to improve their performance in terms of data management. That’s where cloud-based ELT (Extract, Load, Transform) comes in. It provides the ability to extract important insights from data much faster than before, and analyze data sets at much faster speeds.

Make the right choices with the data

It is normal for fast-growing companies to think in the short term, and fix any problems they encounter. This applies to data as well as everything else. Companies often solve short-term problems and create ad hoc data connections. The fact of not thinking about global data management but managing it in a piecemeal way can lead to increased costs and especially in the long run the data will not be optimal and will lead some organizations to make bad decisions because they will not have a global vision. You shouldn’t choose your data management tools lightly, you should find the tool, even the platform that allows for improved automation and streamlining of processes. Data teams must understand the tools they use and their compatibility with their organization’s data stack.

Why is data hosts so important today?

Most organizations still struggle with data culture issues within teams. Data should be a priority and thought about from the start of the business. Having a data supervisor in-house can help with data discovery, identifying what data is there internally and how it can help the company achieve its goals. Companies need someone who can guide them with data. This person must understand the data architecture, but also what the company needs to get from its data warehouses.

It is not enough to have data, members of the organization must understand how to use what is given to them. The entire organization needs to have the tools it needs to dig into that data and understand what data matters. This is where data leadership comes in. Becoming a truly data-driven business requires managing change within the organization. For this, it is not only about technology, but also about personnel. Hiring good data professionals who understand the latest cloud technologies and the business acumen to know what to do with them will be a huge challenge for companies across all industries in the years to come.

Especially in today’s economic climate, data managers need to be able to communicate ROI to all data teams, tools and processes. Many organizations still struggle when it comes to inculcating data literacy across the organization. Data managers need to realize that the biggest data challenge is often people.

In conclusion, organizing a perfect data stack that connects all sources and collects data is just the beginning of the business. As a business leader, you must then ensure that your teams are ready to take advantage of the potential of this data, with the knowledge to get the most out of the tools. The most important thing is to train all teams to use this data to meet the challenge of continuing to grow exponentially over the long term.

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