July 17th, 2019
What qualifies as big data can vary from one company to the next. However, experts agree that you can classify it as such if the volume is so large that the company faces challenges with storing it, if it is created at a rapid pace and if the data is in different formats; such as images, video or documents.
Big data, once collected, can be analyzed to reveal patterns and trends which arm companies with information to make better business decisions, and in the long run to create products and services that better meet the needs of their customers. Unfortunately, this is not always the outcome.
According to a New Vantage survey, only 48% of the 95 companies who undertook a big data project achieved measurable results; even then, they ran into challenges in the strategy implementation phase. Below are some of the common challenges faced with Big Data and how to solve them:
1.Converting Data into Valuable Insights
Data will be of no use if it is not converted in a timely manner - this is particularly crucial when it comes to observing seasonal trends. You need to be able to act on information while it is still relevant.
The solution to extracting valuable insights starts with your big data strategy. When building your strategy, use these digital analytics expert tricks:
- Measure the right things
- Ask stakeholders the right questions
- Identify customer segments
- Articulate your hypothesis
All company data can be vulnerable, and in the wrong hands it could lead to the loss of your competitive advantage. Putting big data specific security measures in place is something companies need to prioritize at the start of the project.
It is not enough to simply rely on already existing security methods at your company. Forego the assumption that data storage software has sufficient in-built security and create a plan to augment it with data encryption, data segregation and identity access control.
Big data has the potential for tremendous growth. Storing and analyzing all that data is a problem that companies find themselves investing mass amounts of resources to solve. When it comes to storage and analysis there are a number of tools that companies can use.
Compression, deduplication and tiering can reduce the amount of space and costs associated with big data storage. However, companies need to toe the line when it comes to scaling up the processing and storing capacities to ensure that performance of the systems isn’t adversely affected.
4.No Staff Buy-in
The challenges big data faces are not just technical, sometimes they are people problems. It is important for a company and its employees to understand why they are taking on a big data project. Without decision makers fully understanding the ins and outs of big data, there is a risk of implementing elements that are unnecessary.
Without understanding the value of big data strategy, adoption from employees will be compromised, and this will lead to a wastage of resources. Extensive research and enlisting the services of a consultant is an important step before the start of the project. Companies need to treat a big data project as a companywide issue. Training and workshops will go a long way in obtaining employee buy in.
5.Integrating Different Data Sources
The data itself can create several challenges, it might be incorrect or contain duplicates and inconsistences. This is compounded by the fact that companies receive data from many different sources. Validating the data and then merging it to produce meaningful reports can be problematic.
There are a number of solutions:
- Use integration tools
- Use data governance
- Have a proper data model from where you can compare data and match records
- Invest in data management solutions
Don’t know where to start with big data?