Big data analytics today is seen by enterprises as a key factor that facilitates major performance improvements and effective decision-making. But successful big data analytics requires tremendous amounts of data to make sense of. Modern day enterprises generate that much of data. However, only 12% of this data are being analyzed by most organizations according to Forrester research. This means they are probably missing many vital insights that could contribute greatly to the organization’s growth.
Analytics isn’t anything new. The technique of processing information to uncover insights has been around for decades. But even today, many organizations struggle when it comes to getting the most out of their data. One of the many reasons for this is poor planning.
That said, here are a few tips to actually make big data analytics successful in your organization.
Cleansing the data should be step 1
Companies that leverage big data analytics generally tend to consolidate data from various sources and start processing them. But a better approach is to cleanse the data first. This may take time when huge data are involved. But taking only the right data sets to get analytics to answer the right questions is a more cost-effective approach in the long run. In addition, this also makes report development easier and considerably reduces complexity.
Tailor the solution with the end-goals in mind
Before obtaining business intelligence, the organization should first determine their business intelligence goals so as to frame the right questions they can get answers for from insights via analytics. Priorities can be anything from operational performance to risk management and understanding customer behavior. Once priorities are determined, analytics solutions can be tailored to meet those end-goals.
The right questions matter
This applies particularly when operational performance is the priority. To obtain insights into business performance, the organization has to determine beforehand exactly what information is required from the solution, frame specific questions, and then ask the data.
Overestimating reports may exceed the budget
Many organizations overestimate the number of reports their new analytics solutions can provide. This is a mistake that can up their expenses in the form of third party development charges. A more cost-effective approach in the long run is to utilize the budget on a self-service analytics solution that allows users to build the reports they want depending on the organization’s needs.
A dashboard for stakeholders and senior managers
Whatever information the analytics solution can uncover should be displayed to stakeholders and senior managers properly. For this, it’s best to prioritize the design of an executive dashboard which can display accurate information in a simple, understandable manner. This approach also ensures that the system will be widely used within the organization.
The existing system should be thoroughly reviewed with the help of specialists of each concerned department and the developers of big data analytics solutions to ensure that reports from the new solution shares the same format as that of the existing ones albeit refined. Table structures and calculations should be standardized thus eliminating inconsistencies which can increase complexity in the future.
Usability of the solution is vital
The solution won’t be able to serve its purpose well if it’s just too cumbersome to work with. If anything, it should be easy to navigate. This is vital if it is to produce accurate information in an impressive fashion, subsequently helping improve decision-making. If using the system requires the user to be tech-savvy with programming skills on top, the solution’s use will gradually decline. The solutions developed should be accessible to non-technical users as well.
Investments should focus on skills rather than technologies
Another factor that contributes to the success of a big data analytics project is staff training. If the teams are trained well to use the system efficiently from day one, the company can expect rapid return on its investment. The organization should essentially value skills to leverage a technology more than the technology itself.
The approach should instill collaboration
To make sure that the solution delivers desired results, the organization’s in-house team and stakeholders will have to work together with the solution developers. This also equips the team with the knowledge and know-hows to leverage, maintain, and upgrade the big data solution in-house.
Augmentation via upgrades
The final tip is an advice to factor in upgrades and their impact. As technologies advance, the solution can be augmented to gather information from multiple sources faster. This in turn supports faster decision-making.
AoT’s expertise in delivering cutting-edge digital solutions including big data and IoT makes us a competent partner you can rely on. We ensure that our clients are fully prepped to leverage the potential of big data analytics for business growth. Send us your queries to explore what a tailored analytics solution can do for your business.