Over the past couple of years, there’s been a lot of buzz surrounding big data analytics. As a result, companies both big and small (across all industries) have jumped on the analytics bandwagon to compete and take advantage of data sets.
Data has also proved to be useful as government, weather, and social data can now be utilized to predict supply chain outages, identify individuals among an ocean of web clicks, and build algorithms capable of engaging with customers.
But it hasn’t been easy, as, in fact, a lot of enterprises don’t understand how to harness the collected data and derive some value from it. Businesses have tried to approach this with a focus on the size of the data and ended up collecting massive amounts of it. But to truly get some value, the size of the data isn’t vital, it’s the ability to collect the right data that makes the difference.
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Big data is not only about data analysis, it also includes data management. Tech giants like Amazon and Google have already been doing this successfully, but other industries are still in their infancy. So this can be looked at as the golden age of big data where each of daily digital interactions get aggregated into massive data sets.
Further, we’re also improving the ways in which data in handled to hopefully gain some fresh and valuable insights that can benefit us. So this is a departure from its early phase of optimizing existing data to collecting data from a wide variety of exogenous data sources to make more accurate predictions.
Faster processing power has also enabled some processes like dynamic pricing to be improved significantly. Further, companies are also now able to conduct business forecasts without any help from outside. This has allowed them to be better prepared for the future of the business.
It’s important as enterprises have to adapt quickly to the constant evolution of the business environments in the digital era. As a result, you can say that the true potential of big data lies in satisfying the changing needs of the consumer in real-time.
But not everyone gets it, retailers are especially guilty of not understanding this vital concept. So how do you create value for your business through big data analytics?
To do this efficiently, half the battle lies in collection strategies and utilizing the most appropriate technologies to engage in big data collection, management, and analytics. Once you have the right database software and analysis tools integrated into the IT infrastructure, only then can you start thinking about developing business strategies.
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Successful big data analytics initiatives always involve efficient project management and sound strategies. Here’s some food for thought.
1. Focus on Strategic Data Sets Only
Although you may have a massive amount of data from multiple sources, it doesn’t mean that companies must examine all of the information. It’s better to strategically identify data that has the potential to lead to valuable insights.
Companies will find it a lot easier if they start focusing on the right questions from the planning stage. But it’s also important to note that it’s all relative to your business goals. Sometimes all the data may have to be analyzed whereas another situation may only require the examination of a data subset.
Targeted strategies can be implemented by segmenting data into categories (like behavioral, demographic, and geographic). For example, marketers use segmentation to understand who’s buying what product and the best approach to target consumer groups. Further, segmentation is also used in the finance and insurance industry to identify abnormalities.
2. Manage Complexity by Determining Effective Business Rules
Probably the most important aspect of big data analytics initiatives is dealing with complexity. To conduct the analysis effectively, you will need to get the users (from different departments that are responsible for different business functions) involved.
Following this approach will enable the technical staff to identify necessary business rules early on. Once the rules have been defined, it can be assessed to determine how complex the solution has to be.
This is also a good time to identify how many staffing hours are needed to generate valuable insights from the input data.
3. Translate Business Rules and Conduct Applicable Analysis Across Departments
Once business rules have been developed using a thorough approach, businesses will find it easier to adapt and revise them accordingly in the future.
Further, communication and collaboration between the project team and the experts from each department is key to simplifying (and speeding up) the process.
4. Permanently Adjust the System to New Requirements
To successfully carry out a big data analytics initiative, it needs continuous attention and updates. This can be something like a revision of database queries and the knowledge of changing business requirements or regular maintenance.
As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements.
It’s also a good way to ascertain if you have built a valuable analysis system. If it can be adapted to the business’ changing requirements, it will prove to be highly valuable.
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5. The End User is Important
It’s crucial that the end user is always thought of during this process. IT infrastructure, storage, and processing massive amounts of data (both structured and unstructured) are important, but it will be ineffective if the system isn’t user-friendly.
During the development and the adaptation stage, it’s always good to consider the fact that different users within the organization will use big data analytics applications in different ways. So the system has to be able to accommodate everyone efficiently to run a successful big data initiative.
Big data is here to stay, but it does have a long way to go. As more businesses increasingly incorporate it into their business processes, it will become crucial to work with the right data that provides real business value.
There aren’t any shortcuts when it comes to running a successful big data analytics initiative. But it can be done and repeated effectively if best practices are continuously followed to keep the project on track.
Companies can no longer just focus on the technical aspects of big data as it’s simply not enough. Business factors also have a major influence on the success of a big data project. It’s the best way to get the most business value from big data analytics. Do you agree?