Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week we’re looking into how your business can leverage big data analytics to reduce operational costs. 
At the core of a well-oiled enterprise machine is a fine-tuned data management process. Organizations with access to large volumes of data have realized they’re sitting on gold mines and are scrambling to find ways to use this data to be more profitable. Data analytics can help cash in on trends and bring down production costs like never before.
Let’s take the case of a cell phone service provider. Every cell phone has a unique identifying number, also known as an IMEI. Any nearby cell phone tower can triangulate the location of that phone by communicating with it through radio frequencies. This is recorded by the tower and stored as data by the network provider for every incoming and outgoing call to your phone. As of 2023, the United States has over 310 million smartphone users, which translates into more than a billion different data entries each day. These huge volumes cannot be stored in regular data centers, nor can they be processed the same as regular data.
The large data storage repositories that temporarily hold these large volumes of data until they’re ready to be processed are called data lakes. Processing and analyzing this data can help organizations solve complex business challenges and optimize output. Here are three ways to do it.
1. Optimize big data processing with AI
The relationship between AI and Big Data has come full circle. Both are vastly different disciplines. However, an AI-model is only as good as the data it’s been fed. Scraping large amounts of freely available data enhances the functionality and capabilities of an AI model. Now, these same AI models can be implemented in data processing and analytics to produce better results. Leading industry tools such as ManageEngine Analytics Plus, implement machine learning to generate accurate predictive analyses and valuable insights for use by AI.
Forecasting business is big business, and organizations benefit immensely from this. Finding and implementing the right AI analytics tool to generate key insights helps your organization prepare for the future and reduce operational costs.
2. Leverage real-time data
The most valuable asset of your organization’s repository is it’s most recent and accurate data. The sooner you process this data, derive insights, and predict trends from it, the better it is for your organization.
Real-time data, also known as events, is a fresh data entry that requires immediate or timely processing. Suspicious credit card authorizations, OTP verifications, customer behavior on e-commerce apps are all great examples of this. Previously, batch data processing was the norm, where organizations would collect and store large volumes of data for long periods, to be processed for separate functions. In our current data-centric age of business, organizations are bolstering their data analytics mechanisms to ensure business continuity and optimize revenue generation. The realization is hitting hard that timely processing of data can translate into big bucks!
3. Keep your data organized
It’s super critical that only the most accurate and consistent data is readily available only to those who are required to access it. To accomplish this, an organization must ensure that large data volumes are well organized. The first step is to standardize and properly format the data. For example, if your organization is keeping records of phone numbers, some entries might have country codes and some might not. But these seemingly minor discrepancies could lead to data processing difficulties. Proper formatting and consistency in this data ensures easier processing..
Having access to and processing large amounts of data can prove to be profitable for organizations in the long run. Responsible usage and processing of customer data using the optimization methods described above are key to running a successful business built around data.
