Knowledge is power, and that’s an indisputable fact. That’s why the global data science industry, which is all about information, is a multibillion-dollar industry with a calculated annual growth rate of over 25 percent. Why is the data science industry growing so quickly? With companies adopting big data, artificial intelligence, and machine learning, data is more critical than ever and provides companies with actionable insights, enabling them to improve everything, from customer experiences to business processes. Indeed, there are no limits to what your small business can do with the right data.
Even though advanced analytics and other fields of data management have become increasingly popular in recent years, the reason it’s growing so quickly is a lot of companies still haven’t caught on to how valuable data is. Yet every day, more companies are realizing the importance of business intelligence, so your company doesn’t have long to get on board. However, many business owners are reluctant to implement digital transformation, as data management does come with its fair share of challenges. Continue reading to learn about some challenges that are unique to managing data.
Data integration can be labor-intensive and time-consuming.
One of the major challenges for business users managing data is data integration. Integration can be a labor-intensive process, and ETL processes require a high level of skill, a large enough IT department, and the right tools. ETL is short for extract, transfer, load, which is a data integration process.
One way to define ETL would be to call it a manual integration process with three steps, which are finding and extracting data from disparate sources, transforming the data so it all has the same format, and loading it into a source system. Data scientists implement data-mining processes to gather data from various sources, and then the data engineer writes a code to format all the data before loading. It’s a process that costs tens of thousands to hundreds of thousands of dollars and can take months, depending on the volume of data.
Data silos make it difficult for companies to share data across disparate source systems.
Another common challenge of data management is data silos. Data silos stem from companies using many different software applications that all generate, collect, and record data. In a perfect world, business users would be able to easily access data from their customer relationship management (CRM), enterprise resource planning (ERP), and API programs, but it’s more difficult than it sounds. That’s why companies must have the right data integration tools to ensure they can easily access data from disparate sources from a single dashboard.
Many companies lack the skills to maximize their business intelligence operations.
Another challenge for many small businesses is they don’t have the necessary BI tools and skills to maximize their data operations. Growing companies rely on data integration platforms that provide scalability, enabling them to add cloud applications to their growing IT infrastructures as the need arises. Furthermore, user-friendly business intelligence platforms often mitigate the need for IT teams and complex training, as they’re vendor-managed.
Big data is huge and only becoming bigger every day, making it a challenge for small businesses to keep up with the ever-changing and growing business world. Some of the challenges unique to data management are the sheer volume of data; the need for proper BI tools and skills; the time, effort, and financial cost of data integration processes; and the difficulty of moving data across disparate sources. As you can see, the challenges of data management are numerous, but with the right data science tools, your company can become an industry leader. After all, data is your company’s most valuable asset, so you might as well invest and make the most of it.