You know those sales calls that you get from software companies, where the eager sales rep asks you what keeps you up at night? Claiming that their solution will integrate your existing systems, reduce your technology costs, generate powerful reports, and improve employee productivity? Seems like an information executives Utopian ideal, doesnt it?
Whether that sales reps company can deliver on all of these promises isnt as material to this article as whether your companys data can be trusted to make important decisions on. The individual data records themselves are innocent enough, they likely have been generated by your customers, employees or suppliers with the best of intentions. Its when they socialize through integration with data from other systems, get mixed up with duplicate records, or are manipulated by employees in various departments of your company that Good data can go Bad.
Struggles with bad data are common across many industries and companies
Running multiple departmental reporting products against applications like CRM, ERP or POS systems have been the go-to strategy for most companies. Departmental managers generally trust their own local data, but prefer to put a wall around it from the enterprise. A recent Experian study, and infographic published in InsideBigData.com showed:
- 66% of managers surveyed say that bad data has negatively impacted their organization in the last 12 months, with 56% saying that they lost sales opportunities.
- Only 2% of businesses trust their data completely.
- 86% of businesses see value in implementing a data quality initiative.
The study also demonstrated that even though over half of companies surveyed could attribute lost sales revenue to bad data, they couldnt get C-level executives to approve a data quality project due to lack of budget availability.
Mistrust in Data from the Executive Suite
Another survey, commissioned by KPMG and conducted by Forrester Research, showed that executives and C-level business leaders in companies around the world generally arent confident in their data management and analytics systems could produce reliable, decision-worthy insights. The study shows half of the businesses surveyed are used to profile existing customers, and just under half are used to develop new products and services to expand their client base.
Though executives in the survey werent bullish about their data and analytics, they were fairly confident in it to at least mitigate risk. It seems they tend to make decisions based on equal parts:
- Experience
- Gut feel
- Advice from peers and direct reports
- Reports from their analytics systems, as a way of confirming their offline tactics described above.
Thirty-eight percent of the executives surveyed had the most trust from the process of data sourcing, or the data which is classified relevant for analysis. The further along in the analysis lifecycle the data would progress, the less trust the executives have in the data.
A Data-Driven Culture Is Just the Beginning
For companies that strive to get their employees to accept the concept of working in a data-driven culture, and to adopt the various business applications which are central to their responsibilities, these survey results are likely troubling.
Its not enough to just have employees to input data, they also need to adhere to interdepartmental standards for data quality. Companies need to provide their customers, partners and/or investors with self-service tools which ensure the data they send through web forms and other inputs is accurate and current.
If companies dont have sufficient financial resources for an enterprise-wide, automated data quality initiative, they should establish ways to manually improve data quality, or establish a phased approach to master data management. The KPMG study suggests that for companies to build trust in their data, they must work through the following seven steps:
- Assess the trust gaps, to understand what it is about the current state of the data as to why it isnt reliable enough to make decisions on it.
- Clarify the organizations goals for the use of data, whether it be to understand existing customers better, to improve operational effectiveness, or reduce risk.
- Raise awareness about the issues with existing data management practices, and engage everyone in the process of improving data integrity and quality.
- Develop an organizational data and analytics-driven culture. Train employees on how to source information about their own departmental/individual performance metrics, and/or socialize key performance metrics on a regular basis.
- Increase transparency by eliminating constraints on accessing data, open the black box with data visualization tools so people can understand data without needing to be a data scientist.
- Provide a 360-degree view of the data by sharing data among communities of employees.
- Encourage employees to be innovative to find new ways to manage and use data to respond to business trends. Apply the findings from data to apply to product design, and to drive organizational change to compete in the companys respective marketplace.
Information is often referred to as a companys second greatest asset, after its employees. When companies dont trust their data enough to base important decisions on it, they increase the odds of making a bad choice. Though budgetary challenges can be difficult to overcome at the best of times, executives should realize that they are missing out on significant opportunities to increase market share, provide better customer service, and compete in their market space.
