In this ever-growing and extremely volatile business-sphere; well-documented information is the key to succeed! In fact, no organization can undermine the significance of strategic assets comprising of information as well as data and when put together, they can write success story of any enterprise.
In fact, in a very recent survey comprising of 140 companies, it was observed that inefficiencies and wasted opportunities resulting due to poor data quality cost each of these companies an average of $8.2 million yearly.
And since, the volume of business intelligence is swelling up; managing and documenting big data has become a daunting task for a lot of organizations. In a recent report published in Xplenty; one third of business intelligence professionals find themselves stuck up as they have to dedicate around 50-90% of their time in just cleaning the raw data. This ultimately affects their productivity and the overall decision-making processes. Managing business data, thus; has become the most important task for any organization looking out to have a well-placed and documented information cache.
Why is there so much hype over data quality matter?
Bad data can prove to be really expensive!
If we go by the 1-10-100 quality principle; the comparative cost of fixing problem increases manifold over a period of time. So, if the cost of avoiding bad data from entering is $1, then the cost of correcting the current problems goes as high as $10, and the cost of fixing a problem after it fails, either within an organization or with a customer, reaches to $100.
And; so going by the saying garbage in, garbage out, unchecked, poor data quality results into:
- Wrong business decisions
Dirty data generates ambiguous reports which can sabotage the best efforts put in by decision-makers.
- Inaccurate sales forecast
Sales statistics derived from bad data can sometimes be extremely overly optimistic or pessimistic; thus creates a very vague picture and thus makes it difficult to forecast.
- Poor customer service
When customer service representatives depend on inadequate or incorrect information, theyre not able to deliver an excellent experience to busy or demanding customers.
Who all are responsible for data quality?
- Everyone who are responsible to take care of database Right from the sales representative who enters the data manually to senior managers who set the data policies for the company.
- The onus lies with every obvious stake holder Sales reps should be extra cautious while entering customer data and managers should put in their best efforts to polish new lists prior to importing them into the database.
However; most of the time, these tasks are ignored; though they are extremely important. Why, you will ask? The main reason for it being ignored at large is that it is very demanding and time-consuming task that requires businesses to be constantly on their toes. They have to get fresh information added on regular basis, even edit the existing ones for updates and most importantly, delete the data that is no longer useful. It is seen that most of the times, this procrastinating task of deleting the data, results into a massive database accumulating all sort of old and unwanted data.
Bad data can cause troubles and irreversible dents to several, or may be each and every industry that one can think of. If you are in healthcare or deal with real estate or have an online retail store; bad data will create lot of issues in managing the quality of information.
Well, if you are neck-deep into unwanted data, let professional data formatting and cleansing services provide you the solution! There are numerous service providers who will deep dive into “spring cleaning” your data by applying best practices, and leverage value of your data.
Best practices prevalent among data managers to ensure your organization is not plagued with Bad Data:
1. Dedicating best resources to maintain data integrity
Since, it is seen that lack of understanding regarding the uses and value of data as one of the primary causes of data errors; and it is here that you will require a skilled service provider. With their expertise and in-depth understanding; they can comprehend the importance of data and then clear out the ones that are no longer needed.
If you are planning to do the data cleansing and formatting on your own; you are certainly inviting lot of time-consumption and putting your productivity on stake. Professionals, on the other hand, have dedicated resources in terms of man-power and technology. A right combination of both addresses all the issues related to data quality. These (human) resource possesses right set of skills statistical included; that drives to successful execution.
2. Getting into the depth and understanding the origins and history of the data
Since, these professionals are no novices; they try to get deeper into the data and learn when and where it actually originated from. With this knowledge; importance of the information is established and it can even help in convincing the top executives about the persisting data issues that your organization actually faces. It is because of this practice that one can learn more about the devastating impacts of bad data on quality of your decisions, since it throws light on it and makes data discrepancies clear.
3. Consolidate data management and business intelligence
Even though you have formulated best data governance policies; they are not enough to secure your data. The massive volume of data flowing through enterprise system makes it all the more challenging to keep up with the data quality at all times. It simply is not feasible to manage quality record-by-record, or even to make an attempt to govern every bit of information collected by an organization.
The key to success lies in identification and prioritizing the type and amount of data which requires constant data governance. When you have a stronger Business intelligence (BI) approach in place; you can keep a tab on which data sets are most likely to be utilized and can be targeted for a quick quality management and governance. A smart data management process can then be put to use for collecting that data.
4. Create a data quality firewall
Needless to say that data is a strategic asset, and the organization should take it quite seriously. Just like, any other corporate asset, the data contained within the organization’s information cache has a very strong financial value. This value increases and relates to the number of people who have to use it in the most effective manner.
Keying in wrong data into data warehouse will not just make it problematic to get clear business insights and collect actionable information, but also will spoil good data. Therefore, you require a virtual data firewall that maintains the quality; this firewall detects as well as blocks bad data at a point when it enters the environment, thus proactively preventing bad/dirty data from contaminating enterprise information sources.
Bad Data is a slow poison which can prove to be lethal in long run and hence data formatting becomes a must!