The adoption of data-driven technology is at an all-time high. Data-driven decision making has become the bread and butter of agile businesses that seek to remain ahead of their competitors. On a daily basis, business leaders review their company's data and run analytics to gain insights that will guide them down a path to success.
This process has become essential to critical decision-making in recent months, developing forecast models and assessing risks. Therefore, having access to complete, high-quality data has never been more important.
Yet, even though business leaders understand the value and impact data can have, 77% of IT decision-makers (ITDM) do not trust their data, according to research by SnapLogic. The distrust comes from a lack of timely, accurate data insights, often resulting in missed revenue opportunities and customer dissatisfaction.
Trust is further eroded by a lack of standardisation of data, as ITDMs reported that 84% of analytics projects had to be delayed because the data wasn't available in the required format. All of this would make ITDMs question the point of spending so much time, effort and resources on gathering data that did not work for them.
Let's have a look at the root of the problem.
What's wrong with my data?
To start, and this will not be a surprise — bad data is a time-waster. But just how much of a time-waster is it? SnapLogic's research uncovered that on average, four hours is being lost per employee per week due to the need to resolve issues relating to preparing data for analysis.
Four hours here and there may not seem like much, but let's put that into perspective. A company with around 500 employees who had to spend four hours every week on a needless task would set a business back 2,000 hours a week. That's about 50 employees' full week worth of work not being done.
No company would continue to employ such people, yet businesses continue using the same time-wasting strategies and tools when handling their data.
Even if employees were at the height of their efficiency, inaccurate data would still undermine critical data-driven decisions. It has almost become commonplace for businesses to take on analytics projects expecting that they will encounter problems with their data that will later need to be reworked. Poor formatting is also an issue, making it challenging to perform timely analytics and deliver useful business insights that can help businesses stay competitive.
Something needs to change. Indeed, over 90% of the ITDMs surveyed agree that a lot needs to be done to improve data analysis quality.
The next barrier is disconnected data. Like missing puzzle pieces, it is difficult to get a full picture of the business when vital parts of information are hidden somewhere, and staff don't have access to complete data sets when they need it.
Data silos and disconnected data are critical concerns for ITDMs. On average, organisations with more than 3,000 employees use 159 different applications or data sources. With so many data sources, it is unsurprising that enterprises are struggling to manage their data.
Where data silos exist, data that should be used in analytics may be missing or incomplete, meaning decisions based upon the analytics are flawed from the outset. It may even be the case that business analysts and decision-makers aren't aware that they are missing data, making it even harder to judge the reliability of decisions they are making.
It's like having thousands of puzzle pieces all mixed up and strewn around the house in disarray. It's time to get data pieces organised. But it's not as hard as it may seem.
Improving data quality and rebuilding trust
Data is still central to the future of innovation. As part of this, businesses need to improve their infrastructure and processes, begin rebuilding their trust in data, and form a reliable data analytics foundation.
Survey respondents noted better data cleaning and management as the most critical areas to focus on when improving data quality. A vital aspect of this is data integration, as it helps to reduce siloed data sets and minimise issues with accessing, analysing and utilising business' data effectively.
Modern integration systems also enable easier migration of data held in legacy infrastructure over to modern data warehousing platforms. These systems can help standardise and de-duplicate the data through the integration process, which means that far less time needs to be spent cleaning data before it can be used to generate reliable insights.
Automation also has a vital role in better enabling analytics success and data trust within organisations. Respondents agree that numerous existing processes surrounding their data storage, management and analytics could and should be automated. By automating repetitive tasks that would take the analyst a significant amount of time, businesses can ensure their employees are focused on more value-adding activities, utilising their unique skills for recovery and business growth initiatives.
Distrust in data is unfortunately not uncommon, but for innovation to truly occur within an organisation, this attitude needs to change. To carry on making decisions with bad data risks a business' health and performance.
Organisational leaders need to embrace the reality that having access to quality data is not impossible, and ITDMs should not settle for sub-par data management and analytics. The technology that enables better data quality, accessibility and management is there, and the sooner businesses utilise it to get maximum benefits from their data insights, the better.