Every organisation in the digital era needs to be gathering data from a vast amount of sources. Information on internal processes, customers, products and more can all play a part in business optimisation, helping to streamline operations, increase productivity and improve customer service.
That said, it’s no longer enough to simply be collecting all of that data – it needs to be of high-enough quality before any real insight can be derived. This problem is one of the most pressing challenges facing organisations today, with InformationWeek’s 2014 State Of Analytics, BI and Information Management survey revealing that 59 per cent of business leaders believe that problems with data quality are the biggest roadblock in the path to successful analytics and business intelligence initiatives.
Defining data quality
When thinking about how effective your business information is, it can help to think about a pyramid structure of data quality. Sitting at the top is the best possible outcome – data that is completely free of defects and rich enough to yield critical insight with little manipulation or human intervention.
Unfortunately, this pinnacle is somewhat unrealistic and impractical, and devoting time and resources to achieve perfect data can be extremely costly. A more effective strategy is to focus on the lower segments of the pyramid, strengthening the value of the majority rather than fixating on those last few defects at the top.
The causes of poor data quality
There’s no easy culprit to point the finger at when it comes to a lack of data quality – every organisation has their own unique processes for data entry and management. Achieving consistency is one of the biggest challenges, however, and Information Age highlights three areas where a disconnect can occur:
- Holding master data across a number of different applications that lack consistency in architecture.
- Relying on end users to update information regularly without any tangible motivation to do so.
- Making additions to individual systems and failing to carry those changes to the database over to all systems in real time.
While the first point may require investment in a more appropriate data warehousing or management solution, the following two are related to organisational culture. Helping each person within the business who interacts with your data to understand the importance of consistency is a crucial element of successful business analytics projects.
Achieving consistency is one of the biggest challenges of big data.
Even beyond the specific management aspect of big data technology, gaining support for improving data quality across the business can have a significant effect on general operations. According to Gartner, data quality can impact productivity across the board by as much as 20 per cent.
Ensuring the quality of your information is adequate enough for business intelligence projects and enterprise reporting may seem complex, but tools and advice are available to make the process easier. Business intelligence is often referred to as a tool, but in reality big data is more than a single piece of technology – successful projects require hardware, software and analytics platforms to derive insight.
Stellar Consulting has the experience and expertise to craft a tailored big data solution for modern organisations looking to improve their offering in the market. Our skilled consultants can guide you towards higher data quality, and assist with implementation of the tools required to keep your database consistent well into the future.
Speak to the team today, and bring your big data technology up to a level suitable for modern business intelligence projects.