08 Mar Do Data Quality Issues Keep Your CIOs Awake at Night?
The world is today driven by an exponential range of different digital technology innovations. Consumers demand digital-first experiences for nearly all of their daily such as shopping, entertainment, travel, finance, and much more. In this regard, data becomes the new oil of the 21st century. However, the quantity of data that enterprises have to deal with today is of gigantic proportions. Studies show that nearly 2.5 quintillion bytes of data are produced by the human race every single day. Add to that estimates of around 25.4 billion connected devices expected to be in the market by 2030 and the launch of 5G networks, enterprises are sure to face an uphill task of harnessing value from this huge gamut of information available out there in different forms.
But are they slowing down their efforts out of fear? Certainly not! Studies show that nearly 94% of enterprises agree to the fact that data and analytics are of prime importance to their growth aspirations. With the range of analytics and big data solutions available today, CIOs of leading enterprises are not too much worried about handling the volume of data that comes in their way. The real issue that often gives them sleepless nights is the quality of data that is being used in their digital applications and enterprise analytical tools.
A decade ago, data quality issues never really made the case for a catastrophic event at any organization though they extensively used big data technology for various business functions. While there were instances such as flawed predictions or revenue leaks, the rate of occurrence was very low, and it was mostly written off as smaller and less significant corporate expenses. But today, the scenario is very much different. As digital takes the driver seat in almost every business and technology like AI, Machine Learning, and the Internet of Things come into the main picture, the focus has shifted from just “data” to the “right data”.
Gartner estimates that organizations lose an average of USD 15 million every year due to poor data quality in their digital applications. This loss could be due to multiple reasons such as failed forecasts, revenue leakages, compliance failure fines, etc. The core of this loss lies in the underlying bad quality of data used by multiple enterprise applications within the company. Quality of data is vital for an organization and must be ensured on three essential fronts including:
- Collection or aggregation of data
- Processing of data
- Management of data
CIOs must incorporate a more comprehensive data handling mechanism to ensure that the large volume of business data that enterprise systems process daily are of the highest quality.
Here are few tips for them to achieve this:
Set SLA’s and KPI’s
Based on the level or depth of how your enterprise applications leverage data from across business streams, prepare a detailed KPI for data quality across every functional unit. Give thorough consideration to how each data item contributes to the organization’s value chain and accordingly prioritize the KPI’s and SLAs for their quality. It is advisable to focus on measurements that can bring value to the insights generated from the data. A few examples could be freshness, completeness, velocity, consistency of patterns, etc. These can help drive the overall data analytics strategy of a business in the right direction.
Identify and assign responsibilities to key stakeholders in every business function or department to ensure that data quality is measured and assured of meeting organizational standards. Make sure the leadership teams are also involved and accountabilities are transparently mapped to KPIs derived for the organization. There should be mechanisms and checks in place to trace accountability and adherence to KPIs at regular intervals of time or at different phases in an organization’s data processing workflow from collection to processing and insight generation.
The best way to ensure compliance with data quality norms and practices is by following a robust approach to data quality management. Organizations can partner with data management experts who can do the data validation, lifecycle checks, monitor quality, and identify failures to ensure that the business’s digital ambitions are powered with quality data every time.
Effective Communication and Awareness Plan
In order to achieve full success with your data quality milestones, it is important to have a proper communication plan in place. This plan should focus on educating all users of your business – from employees to vendors and partners – about the need to collect, supply, and handle quality data from across their daily operational tasks. The risks and losses that may arise due to issues in data quality must be made aware to all stakeholders and appropriate risk mitigation and support information for critical scenarios should be available easily to anyone in the organization.
As the world races into an era of digital supremacy, businesses need to capitalize on leveraging as many insights about their customers as well as target markets through intelligent data analytics. For this to happen, they need to have a well-laid-out plan to acquire data, model it to the right format during collection, identify failure or compliance issues during processing, and use lessons learned to improve their governance and eliminate past errors and inefficiencies. This will ultimately empower them to ensure that all data assets in use at the company adhere to the highest standards of quality with assured value additions to their growth.