With data science coming to the forefront of most industries, machine learning platforms are helping to take raw data and turn it into predictive business models for companies to flourish. While these innovations in data input are constantly making strides, it’s important for companies to not put all their eggs in one basket with data integration. Here are some of the mistakes that many businesses make when it comes to big data.
1. Not putting everything in the cloud
Reference data management can be highly beneficial to a company, as it helps to centralize control of data with consistency and compliance. Gartner big data is high-volume and high-variety with many innovative forms to handle databases of all sizes. However, some companies make the mistake of not placing all of their data sources into a management system.
Inaccurate information can lead to incorrect analytics, leading to decisions that may not be in the best interest of a business. Regardless of the data volume, the accuracy of available data is the only way that a reference data management system will work to an organization’s benefit.
2. Not anticipating machine learning to be disruptive
Machine learning has led to some incredibly useful insights in real-time. However, some companies rely on the design of these systems without truly understanding the process. This can prove disastrous for large organizations that rely too heavily on artificial intelligence to pave the way for the right decision.
Machine learning is a sound investment, but only if properly monitored by data scientists and practitioners with an understanding of these systems. Even if you have less data than you think, you need experts to get a better grasp on these models.
3. Believing data warehouses will solve everything
Data warehouses can solve some major issues in big data technology, but it’s not a one-size-fits-all solution. Warehouses can’t handle any data or information registered from texts, images, and videos. Data warehouses are designed for customer-facing, structured information from a few data sources.
Some companies load all of their information into warehouses and data lakes, but this can quickly go awry without an understanding of the raw data being entered. Semantics and units need to be handled with a particular purpose to assure that a predictive model is accurate. If you put incorrect information in, you’ll get incorrect output.
4. Outsourcing data analytics
Some companies believe that sending their big data to a vendor is the answer to their prayers. While outsourcing to companies like IBM may be ideal for some technology users, it’s important for companies to be prepared to spend on data integration. Oftentimes, companies invest too much into maintenance that they end up losing out on talent within their IT departments.
Variables and an understanding of data management solutions make for a more seamless understanding of their business processes. Even with the highest ratings, it’s important to have people on the inside to get a better understanding of the results. Through outsourced vendors, it becomes a cog in the system.
5. Believing data integration will take care of it
Data integration and data management solutions are valuable tools to have, but just acquiring this software and these analytics doesn’t mean that your problems will go away in an instant. Business leaders invest in highly-skilled employees to deliver results as statements of fact, rather than just crunching numbers through a prototype and coming out clueless.
There is nothing business owners hate more than wasted money. There’s no sense in spending on data management solutions if the problem is not identified, nor is there an understanding of the data volume entering the system or the output from those databases.
