Big data is becoming the cornerstone of the global economy. Enterprise organizations, small businesses, government agencies and every kind of company in between can benefit from collecting, storing and analyzing the vast amount of data created every day by their respective supply chains, and implementing data-driven decisions and initiatives is becoming the way of the world.
The proof is in the numbers. According to Forbes contributor and big data expert Bernard Marr, by the year 2020, 1.7 megabytes of information will be created per person every second. This means that the total amount of data in the world will have skyrocketed from 4.4 zettabytes in 2015 to 44 zettabytes in 2020. For companies around the world, data is being generated through each step of the supply chain, and this information has to be incorporated into their operations.
Organizations can no longer wait weeks or months to generate insights that can be applied to their business operations; instead, this give-and-take of gathering and parsing through information needs to be near instantaneous. When companies begin the transition to utilizing big data throughout their supply chains, it's critical for them to understand the current undertaking and make sure they have the right tools and support in order to create a successful big data strategy.
In other words, they have to be analytics-ready.
"When organizations make the shift to being analytics-ready, it's a huge step."
What Are the Obstacles?
When organizations make the shift to being analytics-ready, it's a huge step. Sometimes, companies run into issues that can prevent them from getting a data project off the ground, or problems may arise that keep organizations from handling it themselves, according to Data Realty’s Chief Technology Officer, Tom Panozzo. There are questions of what tools to use, what kinds of environments to store the data in or even who is going to run the analytics.
Let's take a look at some of the common problems that face organizations when they decide to dive into analytics for the first time:
- Server strength: Sometimes, the servers that you invested in to run the critical business processes for your organization simply aren't strong enough to do the analysis. For instance, finely detailed transaction data can't be processed on just any server; it takes a more powerful kind of computing device to really get enough out of the data.
- Environments: Your computing infrastructure needs to be built for analytics in order to enable the key processes and analysis that takes place. This means you also need the right kind of software to clean the information and create an analytics-ready dataset.
- Resources: It's critical to have a team of experts who know how to use the software and get the most information out of the data. However, depending on the area where you're based, there may not be many high-level analytics professionals available.
All of these challenges have to be addressed before an organization can truly consider itself analytics-ready. After all, if you don't have the right environment for the amount of data you'll be gathering or the right tools to drill down into it, you won't be able to base your supply chain decisions off these insights.
How Do You Become Analytics-Ready?
Organizations can't just leap into the big data game without first making sure they have a clear path ahead. Therefore, they need to partner with big data experts who can blaze the trail and empower their analytics strategies.
At the core, the most successful data analytics projects begin with a pool of collected data. This "data hub” pulls in information from databases, tables, cloud-based tools and on-premises computing environments – the goal being to have one common area built for analytics. In an ideal situation, this single environment enables the processing and analysis of massive amounts of data, allowing organizations to derive key insights without worrying about all of the disparate sources of information.
At Data Realty, we can help you set up this one computing environment and create a strategy to unite all the different sources of data in order to elicit the best insights. We're big data experts – we can add another level to your analytics teams and help you do more of the heavy lifting. Get in touch today to find out how our experts can help you improve your relationship with big data.