Let’s face it. We all make mistakes – me, you and the next guy. It happens. To even the best of us – we are human after all. Some mistakes go by unnoticed. However some mistakes – even if they are seemingly small – might in the long run have consequences that we would rather not have. If small mistakes continuously occur across a certain information management process, it might end up costing significantly. In some businesses it can even mean loss of profits.
To err is human
Towerdata states in their blog that: “65 percent of organizations cite human error as the main cause of data problems. To err is human, but when it comes to Big Data, to err is to lose new business and customer loyalty.”
One example of human error – how a small mistake in excel sheet can cumulate to losses – is Banking giant JP Morgans experiences (according to Tim Worstall, tech contributor for Forbes like told in the article here). The company needed a new value-at-risk model created for a synthetic credit portfolio. The developer created it using a series of Excel spreadsheets. This model, according to JP Morgan’s report, had to be “completed manually, by a process of copying and pasting data from one spreadsheet to another.” The researchers shifted around tens of billions of dollars using these spreadsheets, with no automation or review processes. As it turned out, one of the equations ended up being incorrect, and the bank lost several billion dollars as a result.
So that is the kind of small error I guess you, me or the next guy do not want to make?
To process data is necessary
We need a way to process data in order for it to become the information we need to make (business) decisions. By definition of business dictionary, information means ” Data that is (1) accurate and timely, (2) specific and organized for a purpose, (3) presented within a context that gives it meaning and relevance, and (4) can lead to an increase in understanding and decrease in uncertainty. Information is valuable because it can affect behavior, a decision, or an outcome.” For all these purposes, data needs to be reliable by origin.
When processing data, even with help of IT there is a chance of mistakes when part (even a very small part) of the process requires human counterpart entering data. The data / information can be input in the wrong way (typos, repetition, deletion) or can be put in for example in wrong order or to wrong cell / field. These seem not to be big mistakes, but when in context of figures, numerical information, equations and formulas they might have a huge impact on the outcome of the data management process and it’s reliability.
Many companies use manual data entering when for example using spreadsheets as the (primary) tool for planning, budgeting and financial reporting. According to Time Xender 80 percent of all spreadsheets contain errors. Yet companies continue widespread use of spreadsheets as a relied source of information for decision making processes. And while automation is applied more and more in other processes, data entry in many companies is still a manual task – eating up precious time.
To automate (source) data processing is wise
Companies that make less mistakes gain more market respectability – and by assumption more revenue. Entering data manually might turn out expensive and is inefficient company resource allocation. Manual data entry can be a “boring” task that causes employees to not be careful about avoiding mistakes.
However, data entry as a function is still one of the most critical day-to-day operations for many companies across the industries. Instead of burdening individuals with monotonic, tiring data input work (especially when human resources could be used in to work for more motivating, challenging and invigorating goals) – it might be worth considering to plan automation of data management process to points in process it can be applied to.
A SaaS solution automating previously manually done tasks is an excellent way to eliminate the risk of human errors in data and provide more reliability to the process and the outcome of the process. An automated SaaS solution provides data control without the risks of manual data entry. It enables focusing more on high-level, knowledge work tasks, like for example monitoring unit or portfolio performance and making strategic investment decisions.