Data is the new unicorn of the 21st century and it’s only getting bigger. Big Data companies earned $168.8 billion in revenue in 2018, and that number is expected to grow to 215.7 billion by 2022. Which brings us to the obvious question, why wouldn’t corporations want to use data to grow? The most common answer is because data is so disparate that the obstacles for using data continue to compound.
The only way to learn about an organization is to look at the numbers and data. However, the effort required to collect, parse, and analyze massive amounts of data is no small feat. Especially when all the different sources of data that most organizations use are not integrated. There are several hurdles when it comes to completing this type of effort.
Data integration is one of the first and largest hurdles, mostly due to the number of data sources that many companies use. These sources of data can often be incompatible and full of incoherent or incomplete data sets making them extremely difficult to organize. Often, when trying to collect and catalog this data, formatting updates are required, and this can cause several issues throughout the pipeline of your data. Unless you are a massive corporation with unlimited resources that can afford to build this kind of all-in-one system, it is difficult to collect all of your data in one place to make actionable decisions and continue growth. Luckily, there are third-party companies that specialize in this kind of data integration, along with giving personalized analytics on your data. Data integration is essential to having a complete view of your business and to empower you to make actual data-driven decisions.
Disparate Data from Various Sources
Businesses are always collecting data from a variety of sources/applications, like payroll, HRIS, CRM, emailing, marketing, accounting, billing software, and customer service applications. All of these require their own format and are maintained by different teams within the business. Each team can even have its process for inputting, maintaining, and updating data. Teams could even be entering the same data multiple times without ever knowing it, resulting in duplication of effort and data. Automating all these processes and having an ecosystem of data communicating with the workforce, coupled with Artificial Intelligence (AI) and Machine Learning (ML) results in great actionable analytics for an organization. Not only can your streams of data learn from one another, but you, as an organization, can learn from them.