Tech Insider Tips: BridgeView IT regularly features insight from those in the field with first-hand knowledge of the technology industry. Today’s featured author is Senior Technical Recruiter, Matt Herr.
Business Intelligence. Data lakes. Edge computing. These are technology industry buzzwords that sound exciting in business plans and make for interesting reads, but they are not relevant to every company. It’s great to see business leaders place a greater focus on analyzing their data, but when the right steps aren’t followed, it can do more harm than good.
Consider Your Data and Cloud
Before you can get your hands-on reliable data, and even before you can start shopping for the tools you’ll surely need, it’s critical to first look at what type of data you’re dealing with and where it’s coming from. Just because you read that General Motors or Bank of America is successfully utilizing BI doesn’t mean it’s right for you. Too often, an organization wants to implement business intelligence procedures when they don’t have nearly enough big data to justify it. In some cases, they could be better served by a less intensive relational database management solution with Oracle or SQL Server.
Next, what cloud platform are you using? This will be a deciding factor in which ETL tool you’ll implement. If you’re already in the Microsoft environment with Azure for your cloud, it will likely be smoothest for you to stay in the Microsoft family/BI stack. If you enjoy AWS from Amazon for your cloud, you’ll find a bit more versatility as it works well with many ETL options. Then again, if you’re in a niche and have very specific requirements for how your data passes through your cloud, that will also need consideration. By this point, many business leaders realize they need help working through these decisions along with talent versed in data analysis (more on that later).
Select Your ETL Tool
Now it’s time to shop for the software that can make sense of your data:
- Informatica is one of the most popular ETL tools in the industry, providing easy integrations and a user-friendly interface.
- SQL Server (SSIS) from Microsoft is another great choice, especially for those Azure customers who want the streamlined experience of staying in the Microsoft stack.
- Oracle Data Integrator and Warehouse Builder excel at working with vast data sets, simplifying some of the complexity involved, and interact well with Oracle Cloud.
- Pentaho is a flexible option that eliminates some manual programming and works well with a variety of data types.
There are many other options in the market, but those choosing to go outside the big players do so to meet very specific needs. For example, Health Catalyst is a niche ETL tool that helps healthcare organizations adhere to strict regulations and requirements when working with their sensitive data. Ultimately, considering your data sources, cloud platform, and data format/organization will lead you to the ETL offering that’s right for your company.
Take Analysis and Reporting to the Next Level
After careful decision-making and tool implementation, you’re finally uncovering data insights. Analysis/reporting is the reason why BI exists, and in many organizations, there will be a developer spending 80% of their time solely on building reports. Tableau is the most popular reporting/visualization tool out there, as it works well with most other programs. There are other options of course, such as SSRS from Microsoft if you’re following their suite.
There are two ways to look at your data outcomes. First is traditional data analysis that uncovers general insights to help guide business decisions. Then there’s the true data science aspect, which is where reporting can elevate data analysis to the next level and look at deeper trends. For consumer-facing organizations, data science can help look at buying habits. It can discover where people are on websites when they opt out and can help tweak design to capture more interest and revenue as the customer experience improves. For B2B or other types of companies, data science can lead to artificial intelligence, machine learning, and automation. While those are entirely different strategies in themselves, they aren’t possible without proper data analysis.
Invest in the Right Talent
With an understanding of what a data initiative will require, there’s no choice but to find trustworthy talent that can help make sense of data decisions, create a plan, implement new programs, and run them. After all, a single error in the process can produce erroneous reports that lead to misguided and dangerous business decisions. The issue is that, as an in-demand niche, there is a stark shortage of data talent out there. As a result, many companies have to rely on less-experienced data analysts, ETL developers, data scientists, and others who have a limited background in the specialty.
Even then, it requires significant investment, since, depending on the size of your data initiative, it will take a team of 3-12 people for it to be successful. It can be tempting to hire an outside firm to come in and take full control of the project, but that often becomes the most expensive option of all as costs become harder to contain. After all, sometimes companies consider the cost of tools or programs while neglecting to think about how much money it will take to secure the necessary technologists. The good news is that young developers are increasingly following data career paths, but until they grow their skill sets, talent scarcity will remain.
Are You Making the Wrong Decisions for Your Data?
It’s not an easy question to answer, but it’s one that has to be asked. The best technology leaders will explore blockbuster industry trends like implementing Business Intelligence or data analysis and consider exactly what (if anything) that means for their organization. It can feel uneasy making decisions about your data, but you’re not alone. BridgeView IT can connect you with the hard-to-find technologists you need. Together, the right decisions will be clearer than ever.