With data being touted as the new oil, organizational data governance has gained a lot of importance in the digitized world. When the product launches to purchase decisions to even government campaigns are driven by data, it is understood why data is so important. Almost everything that people do is monitored in some way […]
With data being touted as the new oil, organizational data governance has gained a lot of importance in the digitized world. When the product launches to purchase decisions to even government campaigns are driven by data, it is understood why data is so important. Almost everything that people do is monitored in some way or the other; and data is being collected in some form. This data is being analyzed to gain insights into people’s behaviors and choices, in turn, driving a lot of decisions for most organizations for their products and services.
It is therefore imperative that every organization has a foolproof data governance policy in place. The decisions taken based on the insight gained from data will only help when the data based on which it was taken is reliable. However, there are instances when organizations have had to pay a heavy price, just because their data was unreliable or totally wrong; in simple terms, the data quality was bad. It is also possible, that the insight they sought, showed them a totally wrong picture because of the inherent inefficiency in the organization, storage, or collection of data. Thus, data quality or integrity of the data happens to be one of the most important obstacles that organizations face in data governance.
It is not just important to have a good data governance policy in place; it is equally important to make sure that the policy ensures that the data is reliable and correct in all aspects and its quality maintained throughout. Else, the decisions may turn out to be costly. A Gartner research had pegged ‘poor data quality to be responsible for an average of $15 million per year in losses’. This figure tells a story of its own, about why it is important to pay attention to data quality. Another insight from Gartner as far back as 2007, mentioned that ‘More than 25 Percent of Critical Data in the World's Top Companies is Flawed’. An IBM report has pegged the cost of poor quality data for US companies alone at $3.1 Trillion per year.
There is another hugely worrying statistic that should shake up the data governance policymakers. A Harvard Business Review study published on the topic mentioned that only 3% of the companies’ data met basic quality standards. The article also stated that on average, 47% of the newly created data records had at least one critical error. So, one can only imagine the implications of the decisions driven by such unreliable and erroneous data.
Interestingly, statistics also show that many organizations only have a data governance policy on paper. At times nothing, and in most cases, not everything gets implemented. This is also equally bad as it means that the organization is not serious about the data and the insights to be gained from it. Ignoring data governance is also equally as bad as having bad quality data. So, it is important to get these data governance aspects right for organizations to make the best use of data to ensure business growth, customer delight, and loyalty, and stay ahead of your competitors. On the flip side, when data quality was ignored, organizations have lost out on reputation, opportunities, and of course, have taken big dents in their finance as well.
Another hindrance in data governance, are the data silos that exist within organizations. These data silos result in data duplication and also impact the insights gained out of it. Data silos result in a situation wherein, data exists in some form in some unit/department of the organization, but it is unknown and unavailable to others in the organization. This can hugely impact and cloud the decisions taken within the organization and makes data governance ineffective.
The reasons for data silos are multiple like cultural, ignorance or oversight, technical, etc. However, the impact of not having a single data source within the organization that is equally accessible to all results in a lack of 360-degree view that is important to analyze the same. Duplication may not create confusion, rework or productivity loss, and lack of revenue, but also affect the overall data governance and decisions driven by the policy.
Data transparency is as important as data integrity and it is a must for all stakeholders to know where and how the data they handle, comes from. Transparency of data also improves collaboration and visibility within the organization. The lack of transparency in data may be due to data ownership issues which result in the creation of data silos, which creates other issues. It also means that data analysis also does not happen within the silos, as decisions based on such flawed insights would prove disastrous.
Handling data transparency issues within an organization come under effective data management. They would have to work out policies that allow the sharing of data without security and compliance being compromised upon. Data would have to be treated as an important asset of the organization that needs a central approach from the top. Many organizations have evolved roles like a Chief Information Officer or a Chief Data Officer to cater to such needs. How these roles evolve a 360-degree strategy that takes all aspects of data management into consideration in arriving at their strategies and also ensure foolproof implementation, is what would decide the way forward.
There is also a host of data management or data transparency tools available that organizations can put to good use. However, the tools by themselves may not fully resolve the problem. Awareness about data management needs to be created among the data users and owners alike.
Data governance is here to stay; however, a lot of statistics around the same does paint a grim picture of its mismanagement. Many issues contribute to the current sorry state of affairs including:
But, it is obvious that organizations have realized the power of effective data mismanagement and they have lots of examples to go by. The role of a data officer is now being given the same importance as that of a financial officer. Data is being treated as a valuable asset and is getting its due; when data is given its rightful place, it starts giving results. Consistent results can be achieved based on right and timely insights.
It can be seen and felt across the organization, from the top to the bottom. It can be felt by the employees as well as the customers, and it can be seen in the reputation of the organization and the rise in its value in the eyes of all the stakeholders.