Erin
Introduction and overview
- Central topic of trust is something we have talked a lot about this week
- When you think about some of the themes we have talked around this week with advanced analytics, using data to enable decision making and making data this abundant resource as we heard in the keynote, we first have to trust it. And unfortunately, our research right now shows that most companies don’t trust their data.
Abstract 1: Revenue, reputation, regulation: there’s a lot on the line when it comes to your data quality—it can make or break your company. While businesses today talk a great deal about being data-driven, many of them lack the confidence in their data necessary to drive new initiatives. Given that C-level executives believe that 33 percent of their data is inaccurate, it’s easy to understand why. Do you fully trust your data to make important decisions?
Experian Data Quality’s 2017 global data management benchmark report reveals that more than 50 percent of organizations globally say that a lack of trust in their data contributes to increased risk of non-compliance and regulatory penalties in addition to a downturn in customer loyalty. To become truly data-driven, organizations need information they can trust—and that starts with their data quality.
Erin
To start us off, I am going to talk about some recent research on data management that we conducted earlier this year. We released our global data management benchmark report in January where we surveyed 1,400 people from 8 countries about their data management practices and the impression of data within the business. While there were many trends that came out from this research, one of the top themes for me really centered around this idea of trust in data.
Today we are living in a world of data. It is everywhere and it is affecting many aspects of our businesses, but also our individual lives. When we use it correctly, it can really make some positive changes. It can help us make more money, improve our reputation, help us comply with regulations, etc.
There’s a lot on the line when it comes to your data, and the TRUST you have the quality of your data will determine your ability to excel in using data. That is the overall theme that we are going to talk about today, what is people’s perception of data and how can they improve the data to better leverage it.
Erin:
As part of our study, we asked organizations about their use of data in powering business objectives. By and large, we found that data drives a lot of top business opportunities, and we are using it a lot. In fact, more than 80% of organizations say that they believe data is an integral part of forming their business strategy. The chart shown on this screen demonstrates how businesses currently leverage their information to power opportunities. What’s immediately clear is that a majority of organizations are using their data to increase revenue and to serve their customers better. Below that, we have things like enhancing marketing efficiency, reducing risk, finding new revenue streams, enhancing new initiatives, and complying with government regulations.
When I went to analyze these results, I found it interesting that when you look at some of these other areas, finding new streams of revenue was much further down, particularly when “increasing revenue” is identified as a top driver. Why is that? I believe that this is an indication that businesses today are using data to become more efficient in their operations and to expand their existing business opportunities.
Erin
I think it is important to keep in mind that the only data worth having is trusted data. If you don’t trust it to make decisions and to improve your operations, then why do you have it.
While most organizations around the world say that data supports their business objectives, less than half of organizations trust their data to make important business decisions.
Now that is a big problem if you want to be a data-driven business. It means that the decision-making processes is far more nebulous and potentially risky.
Especially when you consider this next stat.
Erin
52% of organizations say that they rely on educated guesses or gut feelings to make decisions based on their data. This guesswork is contributing to an increase in risk in the organization.
So why do we see this lack of trust?
Erin
We found that human error is the most common cause of inaccurate data at organizations. That has been the case for several years across our study. However this particular question was interesting this year.
The prevalence of human error has decreased by nearly 23% over the past year. In my opinion, this tells us that organizations are getting better at training their workforce to uphold data standards or that they have implemented adequate technology to prevent human error.
However, there are certainly other challenges besides human error. Although most organizations say that human error is the biggest cause of data inaccuracies, we believe the root cause is a general lack of strategy for building a business case around data quality. We saw a third of businesses say insufficient budgets were a problem, and that is up 11% over last year. We see that businesses struggle to articulate the true impacts of poor data quality and this is contributing to these insufficient budgets and creating an ongoing cycle of fewer tools and more manual processes, which also leads to human error.
All of these challenges lead to a high degree of distrust in information.
Erin
Well it is actually pretty high. Global businesses believe 27% of their data is inaccurate in some way. However, our study revealed that c-level executives have a higher degree of distrust in their data than those in other roles. On average, they believe 33% of their data is inaccurate, which can undermine their ability to make strategic decisions.
So does that mean that senior leadership is just going to start putting tons of money into data investment? Well maybe, but I think it is doubtful. We believe that although senior leadership conceptually understands the value of good data, the lack of a solid data strategy, around metrics in the value of data and that is preventing them from seeing the true benefit in making long-term investments in this area.
Erin
Roger, since you have helped many of our customers in their data management projects and with helping them build a business case for improvements, I was wondering if you could spend some time around how to get buy-in for data improvements, particularly at the executive level?
Roger
To have that level of trust in information, you need quality information and you need a management process around data that gives you that level of trust. The challenge is that most businesses don’t have the right structure in place today.
To get that level of investment, you need buy-in.
Roger
A successful business case for data quality should contain quantifiable evidence. If you’re stuck, ask yourself the following questions:
- Can data quality issues be linked to wasted time? If so, measure the time taken by inefficient data processes. Can this be reduced by implementing change?
- Do data quality issues directly impact the resources who work with data? If so, map the resources that are either required to manage data quality or are dependent on good quality data to correct the problems.
- Do data quality issues cost the business today, or do they have the potential to do so down the line? If your answer is ‘yes’, try to understand the negative impact that current data quality processes (or the lack of) can have on your financial bottom-line.
- Do data quality issues make your business processes inefficient or unachievable? If so, then you’ll want to try mapping data quality issues to your business processes, identifying specific workarounds that are costing the business.
- Can data quality issues prevent the start of or completion of strategic projects the business has planned or already begun? If this is a problem for you, you’ll want to map data quality issues that may directly impact the success criteria of any strategic initiative, either directly or indirectly related to data assets held by the business.
Roger
What should you do to ensure data quality is not an IT-only program?
Leverage individual stakeholders from other business areas to help secure funding and to provide the required subject matter expertise for the proposal. Our study revealed that ‘a lack of budget’ and ‘a lack of knowledge’ are cited as the top two challenges to implementing a data quality initiative. So by involving stakeholders from the business, you can possibly secure additional funding from their departments, as well as leverage the first-hand experience of business users to identify measurable impacts. After all, who better to identify the impact of poor data on a business initiative than the business user?
Roger
These are the typical people involved in building a business case based on our research.
Roger
You also need to make your story around data relevant to leadership
When framing the tangible impacts of your data quality business case, consider your audience and tailor your story to meet their interests. Identify who the influencers and decision-makers are, what departments they’re from, and what their goals are. Then, rather than going on about metrics showing the uniqueness and completeness of your database, show why that impacts the Finance department's ability to collect on invoices or your marketing team’s ability to reach their most valuable customers. If your decision-makers sit at the C-level, remember that they’ll want to see clear metrics relating your data quality program to broader objectives for the business, such as operational performance, financial performance, customer experience, and regulatory compliance.
Roger
Another challenge we identified through our research is that the timeline for having a proposal for data quality approved and implemented can take a fairly long time.
To get buy-in, you want to set a timeline for success. How long will this project take and when will you expect to see results. Then communicate that timeline to any stakeholder involved.
Erin
For Idaho, providing top service to their residents is at the heart of what they do. And they were struggling to meet that goal because information on their constituents was spread across siloed databases and it prevented them from having a consolidated view of their residents.
They have a legislative mandate to have one record per DMV customer. They received the ability to collect more information from constituents to try to start to consolidate records, but once they had the individual information, they needed to get that single view. To do that, they needed to improve their data management.
The department choose Experian as a vendor to provide address verification at the point of capture, but then also used the data matching software to find duplicates and data profiling to solve for operational demands and understand what was going on with the data.
Their goal is to eventually get down to fewer than 3 million records and they are confident they will achieve this goal.
The benefits of the tools go far beyond just reducing duplicate records. The department has become more proactive in their approach to data quality and have really empowered their staff to use data assets more efficiently. They can more easily bring recommendations forward to the department’s data governance council for consideration because they have the metrics and data to back-up that suggestion.
Erin
For Idaho, providing top service to their residents is at the heart of what they do. And they were struggling to meet that goal because information on their constituents was spread across siloed databases and it prevented them from having a consolidated view of their residents.
They have a legislative mandate to have one record per DMV customer. They received the ability to collect more information from constituents to try to start to consolidate records, but once they had the individual information, they needed to get that single view. To do that, they needed to improve their data management.
The department choose Experian as a vendor to provide address verification at the point of capture, but then also used the data matching software to find duplicates and data profiling to solve for operational demands and understand what was going on with the data.
Their goal is to eventually get down to fewer than 3 million records and they are confident they will achieve this goal.
The benefits of the tools go far beyond just reducing duplicate records. The department has become more proactive in their approach to data quality and have really empowered their staff to use data assets more efficiently. They can more easily bring recommendations forward to the department’s data governance council for consideration because they have the metrics and data to back-up that suggestion.
