See These 7 Signs of Poor Data? It’s Time for Data Governance

Businesses do not realize how much their poor data is currently costing them. Gartner reports that on average, organizations believed that their bad data was costing them $15 million per year in losses, and not surprisingly 60% of those surveyed didn't know how much bad data costs their businesses as they did not have metrics in place for the process.

At To-Increase, in the past 17+ years, we have helped 2000+ customers solve different problems, from data synchronization to analytics and business intelligence. And during the last few years, we have strengthened our data governance portfolio with solutions such as MDM Studio (master data management), Data Quality Studio (data quality management), and Data Entry Workflow (data entry management). Therefore, we have worked with customers and understand the challenges they face due to poor data such as operational delays, data duplication, and bad business decisions, and have helped them digitally transform their data governance processes after implementing our solutions.

If you are still on the fence about the impact of bad data on your business, it might be wise to look for the signs mentioned in our blog. This will help you gauge whether bad data is impacting your business and then you can decide whether it is time for you to consider working on a data governance strategy to improve overall data-related processes across the organization.

7 Signs that indicate that you have bad data
1) Data silos

When your application landscape and data across business systems are not connected, then you have islands of data that need to either be manually transferred or probably get left out. Either way if you do not have data synchronized across systems, you cannot share recommendations in time, you might miss opportunities and that also leads to data duplication and human errors with manual data processing.

2) Resources spending too much time on data

If you do not have any data governance practices in place, your team will be wasting their time assembling data from various systems and probably reaching out to different teams to source and verify that data. If you had tools for data synchronization, data entry, and master data management, your data would automatically flow between systems with data entry access for just the responsible data owners while data quality rules can ensure consistency, accuracy, and validation. Automating data processes across the organization with a data governance strategy and the right tools to support your teams can free up the time your resources spend on cleaning, re-entering, and fixing mistakes and this will also translate to greater efficiency in all your business processes.

3) Cannot derive meaning from data

If your data is not clean, you cannot derive any meaning from it which means it is difficult to deduce any business intelligence that will help you make impactful business decisions. If you are in the above situation where your teams are spending way too much time re-assessing spreadsheets and fixing problems, then they definitely do not have any clean data to analyze. If you could use actionable insights derived from clean data you can increase revenue.

Additionally, to derive business insights that can point to a pattern, that can help you make confident decisions about goals, estimate patterns for consumer consumption, and justify a return on investment, you need the right tools to support you. If you use a system that is difficult to gauge or cannot manage high data volumes, then you need to take a step back and figure what is not working for you.

4) Data processing is tough even though you’ve hired specialists

You hired data specialists without investing in tools that complement your data governance strategy and expected miracles, but you are nowhere near your goals. These data managers spend most of their time sourcing, cleaning, and preparing data rather than deriving business insights and looking for patterns that can help you work on solutions to customer problems. So, while your investment in a data specialist is good for your organization, it would be more beneficial for both your organization and for them to spend that time doing what they were hired for instead of working on cleaning up and assembling your data.

5) Errors and missing information

With manual data processing there will definitely be human errors but without any tools in place to call them out, the chance of these mistakes going unnoticed increases. There is a possibility of there being different information for the same product or customer in different systems or even missing data.

Additionally, if you use tools for inventory management, for example, that round-up data, again you have another conflicting source of data to manage that does not corroborate data from other systems.

So, when it is time to report back to your department heads, you might have teams sharing inconsistent reports and that makes it difficult to make calculated business decisions. A sure-shot way of dealing with this challenge is to have data processes in place that ensure consistent and accurate data can be published across the organization using a tool such as MDM Studio.

6) Missed opportunities

If you had clean data that helps you derive valuable business insights, it might be easier to see which products are being used and where lies the window of opportunity for a new product launch. If your data is not synchronized and you have no holistic view across systems, it might also result in lost revenue. For example, if your data for an order is not communicated to your logistics team in time, you might lose revenue and it will lead to an unhappy customer experience and loss of goodwill.

7) No process to validate data

If you have no processes to validate data, how can your organization rely on that data for reporting or for deriving business insights? It is possible to set up web services that can validate data and save your team members time if they have to manually check for the authenticity of the data. Our Data Quality Studio tool allows you to do that.

Additionally, some tools such as Data Entry Workflow allow you to set up approval processes and call out errors during data entry. Such a process saves your employees time spent coordinating and getting approvals as manually scanning file after file to check for errors is not always possible.

What is the road ahead for your organization?

If you see the signs we have mentioned in this blog and your data is not checked for the six pillars of data quality which include accuracy, consistency, validity, completeness, timeliness, and integrity of the data, deriving any meaning from your data will be difficult.

According to a report by Forrester, "less than 0.5% of all data is ever analyzed and used". Additionally, their report states that Fortune 1000 companies that increase data availability by 10%, could increase their revenue by an estimated $65 million.

If you want to change the current state of your data, you need to analyze your current challenges and then consider a data governance strategy for your organization. To help you in your journey of becoming a more data-driven organization, our beginner’s guide to master data management is a good place to start to better understand one of the crucial aspects of data governance. Download the resource from the link below to learn how you can streamline your data processes.