It is possible that at some point you have run into the term ‘big data’, whether that was as a professional or simply browsing the web. Big data refers to large, complicated datasets that exceed the capabilities of standard data processing software. But what is big data exactly and what does it mean for businesses?
Well, most digital businesses use the term to describe various data processing methods, such as predictive analytics, which allows analysts or data scientists to derive value from collecting and analyzing online data.
Big data projects in the US healthcare system, according to a McKinsey estimate, “could account for $300 billion to $450 billion in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs.” However, an estimate by IBM found that inaccurate (or bad) data costs the US economy $3.1 trillion annually.
It’s safe to say that data analysis and processing has enormous value. For a business to truly unlock the opportunities of big data, they first need access to vast and secure storage, the processing power, and finally - someone with the skills to make sense of it all. Someone like a data scientist.
The value of data in your business
Data-driven performance management focuses on procuring real-time insights into how a business and its respective departments are performing. These insights allow analysts to identify inconsistencies, areas of improvement, areas of success, and any problems the business may be facing. This encourages managers to create solutions that are backed by data.
But, data-driven performance management is just one of many vantage points. Another example is the use of predictive analytics. Predictive analytics allow business executives to understand customer behaviors, predict trends, inform strategic decision-making, and even decrease costs.
How to make data-driven decisions
Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. Most companies are aware that, in the absence of data, bias and erroneous assumptions can cloud judgment and result in bad or inaccurate decisions. Simply put, decisions based on gut instinct without any data to back them up can cause mistakes and losses.
Companies must ensure that the data is clean, accurate, and up to date, but even if all those boxes are ticked, the data analysis and the results it provides must be fair, representative, and free of bias. It is important to make sure the analysis factors in the complicated social context that could create bias in conclusions. The analyst must think about fairness from the moment business data is collected to the very end of the project.
The key to successful data-driven decision making lies in figuring out the exact needs and expectations for each project. A data scientist will typically ask themselves the following questions:
- What kind of questions is the company looking to answer?
- What are the current challenges inside the industry or competitive market?
- What data sources will I be using to address these questions?
- Who is asking for this analysis, and what will it be used for?
And so on.
However, these questions aren’t limited to the data scientists and analysts. Business executives must also consider various key issues to turn the analysis project into valuable insights, such as:
- Identifying which business areas and departments will benefit from the data;
- Finding the right data (what type, internal or external, how much, the quality);
- Identifying what kind of results the company is looking to achieve;
- Allocating the appropriate amount of resources for the project;
Of course, the list hardly ends there.
While these goals may seem simple at first glance, CAOs, CDOs, and business executives in general all agree that it’s anything but. Obtaining data, devoting resources, and procuring the necessary analytical tools takes time. Most executives are in agreement that having the right data consultants for the job makes the whole process smoother and more efficient.
Jooble makes it easy to connect the right people with the right projects. Looking to transition to data science? Check out these postings for Junior Data Scientist roles.