The value of data and analytics in business
The value of data analytics in the business environment is plural and extremely extensive. To begin with, it is essential to understand that the quality of your decisions depends on the quality of your data. In other words, at this point in time, not using data and analytics as a mainstay of the business decision-making process is simply doing it wrong. It is like taking an exam without studying or diagnosing a patient without asking about their symptoms.
Data is information and information is value. Data-driven decisions are a necessity.
The truth is that data and analytics are complex and often require external intervention in companies that do not have a team of professionals dedicated to data analytics or data science, but the results justify the investment.
The results of a data strategy can be higher profits, a new business model, new customers or even the launch of a new product or service. Beyond the most notorious examples, the truth is that taking advantage of the business value of data makes a company improve its performance. Moreover, the consolidation of a data-driven culture is fundamental to a company’s digital transformation.
Ultimately, organizations need to be smarter about understanding what outcomes can be improved and what investment in data and analytics will drive those outcomes. The list is endless: it can be an investment in artificial intelligence, in data visualization, in tools like Power BI… but the first step is always the same: to stop thinking about data as a secondary asset to make it a cornerstone within the organization and part of the company’s daily activity.
The good news is that, according to Andrew White, Gartner’s vice president: “The potential for data-driven business strategies and information products is greater than ever.“
How to consolidate a data strategy to leverage your company’s data?
As in most cases, change starts at the top. The first step to implement an effective data strategy and start building a data-driven culture is for senior management to understand the value of data and start asking the right questions:
- What data do we need to make informed decisions?
- What do we need to know?
- With this data, how can we improve the value propositions we offer our customers?
- With the information we have, how can we improve internal processes?
- How can we use the information to transform our business?
Simply asking these and other questions is a huge step. Answering them is an even bigger one. The third step is the adoption of an analytical reporting system and data literacy training for the entire organization.
Any data strategy must take into account data quality, data governance and data literacy. These three pillars will help you sustain the success of any investment in data and data analytics.
Steps to create a data strategy and leverage the value of enterprise data
According to our experts, the most important thing to create a data strategy is to be clear about the strategic part of the process. In other words, technology comes after the strategy and not the other way around. Obviously, deciding which information and analytical systems to invest in is an important decision, but what will most condition your corporate data strategy is its approach.
A data strategy should be based on the company’s mission and key business objectives. That is, executives must decide what outcomes they want to prioritize and transform them into business objectives that will guide the data strategy.
Without the right approach, the technology investment will not deliver the desired results.
On the other hand, data strategies must be based on 4 principles:
- Data governance
- Data analytics
- Data literacy
- Data quality
How do we design an efficient and well-thought-out data strategy? Find out by downloading our e-book! You will find the 4 steps that any company should follow to create an efficient and modern data strategy.
Data must play a leading role in a modern, digital corporation. Creating an efficient data strategy, focused on business goals and outcomes and addressing the four pillars of a data strategy —strategy, literacy, data governance and data governance— is the way forward.