Indra's digital transformation company shows that data analysis is the first step towards companies' data intelligence, but only process governance brings lasting and efficient results in terms of business
Advanced data analytics is being applied in various industries as a trend, but despite this, not all companies are able to achieve as satisfactory results as they expected due to the lack of control over these processes. A Harvard Business Review study of 75 companies last year showed that only 3% of them had basic data quality standards – which has a significant impact on making strategic business decisions.
According to Minsait, an Indra company, this is because many companies still believe that advanced data analytics is the key to providing quality insights, rather than taking a step back and investing in effective data governance. “It is necessary to go beyond analytics and build systems based on algorithms, capable of establishing the rules with which to work with the data, so that it is then possible to adapt this information to what the business areas need,” he says. Wander Cunha, head of Minsait in Brazil.
The benefits of this strategy are varied. By adopting algorithm-based processes, companies can process information more quickly and make decisions through the accelerated ability to classify, predict, simulate and optimize activities.
To reach this result, Minsait points out four essential steps that companies should pay attention to:
1. Automation
Have knowledge about the automation used in the organizational environment. Automated processes are often seen – the purchase and sale of assets on the stock market, in which transactions are carried out simultaneously are examples of this – but knowledge about the technology used to make these decisions is usually less controlled by the organization. As a consequence, this kind of intelligence can be applied in a way that generates a poor result for the business or, worse, affect it negatively.
2. Continuous learning systems
The artificial intelligence models in the organization must be well documented and systematized, this being a task for both the strategic part and the business area of the company. Minsait points out the need for “Always on” systems, always identifying new threats or opportunities, generating dynamism for companies.
3. Security in the use of data
It is essential to have control over the data you are dealing with: where it comes from and what it is used for. This control must be decided in two phases: the first, when establishing adequate filters that will determine the data to be collected, respecting corporate regulation, and, the second, in the description of the information obtained.
4. Get financial value out of data
Once you've gathered the data, it's critical to keep in mind the financial impact it can have for the company. Adequate data governance can bring integrated systems, capable of reducing new product development deadlines, increasing productivity, among others, achieving significant cost savings.
In addition, companies that can generate value from data analysis are considered more profitable. “Over the past five years, there has been an increasing distance in values from companies that use effective data governance to those that don't. This is because the application of technology becomes a company management philosophy, adding more value to its name”, concludes Wander.
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