In today's world, business decisions —actually, any kind of decisions— are data-based. However, having a lot of data does not necessarily mean making a lot of good decisions. In this case, as in so many others, quality comes before quantity.
The advent of Big Data triggered an extraordinary technological revolution that significantly impacted all spheres of our lives in ways that we now consider common. In the business world, Big Data meant the expansion of the information available for decision making and the development of business strategies.
Today, making decisions based on data, also known as data-driven decisions, is practically a necessity for companies.
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However, since Big Data began to materialize with the appearance of the Internet and the World Wide Web (WWW) in 1989, data production has continued to increase year after year. In particular, corporate data is increasing by 40% every year.
That's good news, isn't it?
The statement "the more data, the more information" has its logic. However, the vast amount of information also creates problems, even to the extent that too much information leads to misinformation.
Today, informed decision making and business intelligence no longer depends on the quantity of information —all companies have sufficient data— but on its quality.
Paraphrasing the previous statement, the more data, the more complicated it is to manage it and ensure its quality.
In this sense, the production of knowledge, insights and business intelligence is now subordinated to data governance and data quality processes. Now more than ever, it is essential for companies to focus on data management and ensuring data quality.
Howard Rosen, a healthcare innovation consultant, makes the following reflection in Forbes magazine: "Fundamentally, for data to be of any value regardless of its form, it needs to be considered “good” data that, when analyzed, could result in actionable strategies [...] What is crucial for decision-making is non-biased, accurate data. Ascertaining that data is a process and certainly not something with which you can simply 'make do.' Otherwise, you risk making decisions that could negatively affect your business in the near future or down the road."
In short: the quality of your decisions depends on the quality of your data.
Evaluate and improve the quality of your data
Before analyzing the quality of our data, it is essential to resolve other issues that are related to data quality. First, we must explore what data we have and where it is located. Too often, companies keep data and files that they don't even know they have. Duplicate data or not knowing where it is or what it is for is another common problem.
On the other hand, it is essential to check who has access to corporate data. Not all organizations have an easily accessible data structure and many employees do not know where to find the information they need to carry out their work in the best conditions. Without a doubt, data governance and data quality go hand in hand. If we do not have control of our data and manage it properly, ensuring its quality will be extremely complicated and ambiguous. Processes such as ETL and the application of data governance measures are a must.
In terms of assessing data quality, there are many factors to consider: the source of the data and its reliability, the nature of the data being collected, its format, how it will be used, etc. In another article in this blog we have already reflected on the fact that data is not information and information is not an insight. Only quality, validated and useful data can be transformed into insights.
When assessing the quality of our data we must ask ourselves questions such as:
- What is the degree of reliability of the data?
- What is the degree of accuracy and the probability of error?
- Is data consistent and structured or is it random and unstructured?
- What is its relevance, timeliness and applicability?
- Does our data set cover what we need it for?
- How comprehensive is data and is it sufficient for practical analysis?
Once we have ensured the quality of our data, we need to analyze its role in the decision-making process and ask ourselves whether the decisions we make are really based on the data or, on the contrary, our data analysis capabilities are not meeting our information needs.
Making data-driven decisions is not as easy as it seems. In fact, recent research concludes that 62.2% of companies have not yet managed to implement a data-driven culture. In other words, despite having more data than ever before, organizations are still unable to base their decisions, strategies and processes on the data they have.
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Far from being a new problem, organizations have been struggling with this issue for as long as data analytics has existed. In research conducted in 2013, 41% of data analysts surveyed stated that their biggest challenge when working was accessing or integrating data. In 2014, The New York Times published an article reporting that data analysts spend 50-80% of their time collecting and preparing data before they can explore it for useful information.
Unfortunately, the disparity between the time it takes to ensure data quality and the speed at which executives need the information means that decisions end up being made before the information needed to make them is available, which magnifies the likelihood of making the wrong decisions.
In this regard, it is necessary to establish a global and comprehensive data strategy and raise awareness of the need and importance of good data practices at all levels of the organization. How?
- Communicate the value of data for the decision-making process and the need to regularly assess the quality of data and sources.
- Reinforce the company's mission and emphasize that all decisions made lead to that mission.
- Explain the processes that facilitate data access and data analysis and encourage employees to follow them as an ongoing business process.
Whether we like it or not, data governs many aspects of our lives, especially in the business world. Companies' necessity for information is growing, as is the amount of data available. However, more data does not translate into better decisions.
Data governance and data quality processes are crucial for the decision-making process. It is also important to discern which data is necessary for specific decisions and which is not.
If the goal is to improve our actions and decisions, the first step is establishing the roadmap of our data strategy.