Inside the circle of trust - Data management for modern enterprises

Name: Inside the circle of trust - Data management for modern enterprises
Uploaded: 2017-04-10T14:18:40Z
Description: Inside the circle of trust - Data management for modern enterprises

HI EVERYONE, thanks for joining us today. My name is Sean Coombs, and I am a senior content marketer here at Experian Data Quality. WITH me, I have Erin Haselkorn who manages our analyst communities as well as public relations.

TODAY, we’re going to talk about TRUST… Specifically, how trust relates to your organization's DATA and data MANAGEMENT practices. Do you FULLY TRUST your data to make important decisions? WELL, today we’re going to find out!

After today’s presentation, we will answer questions from the audience, so please use the Q&A box on your screen to submit questions at any time during the session.

During our session, Erin and I are going to share our findings from a UNIQUE GLOBAL STUDY we recently conducted. We’ll talk about how businesses use their data to POWER opportunities, why TRUSTED data is essential for SUCCESS in these areas, and what CHALLENGES you might face in achieving trusted data. Then, we’ll look at current data management practices and provide our insights for how you can build CONFIDENCE in your data—and, ultimately, trust your decisions based on that information.

BUT FOR NOW, I’m going to hand it over to Erin to tell you all a little bit about our study.

Experian Data Quality has once again conducted a survey to look at global trends in data quality. This study looks at how data practitioners are leveraging and managing their data to generate actionable insight and how data management practices are changing over time.

Produced by Loudhouse for Experian Data Quality in November 2016, the study polled more than 1,400 people across eight countries around the globe. A variety of roles from all areas of the organization were surveyed, including information technology, data management, marketing, customer service, sales, operations, and more. Respondents were chosen based on their visibility into their organization’s customer or prospect data management practices. Organizations that were surveyed came from a variety of industries including IT, telecommunications, manufacturing, retail, business services, financial services, healthcare, public sector, education, utilities, and more.

I think to start off, the real theme that came through to me in this data is trust. 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.

Sean, at this point I will pass it over to you to talk about how people are using data.

THANKS, 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 SHOWS JUST 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 REGULATION.

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.

HOWEVER, I think Erin would agree that there is plenty of room to change this as we increase the TRUST in data.

SO … while we know that data is used to power business OBJECTIVES, we were left wondering how CONTACT data plays into these initiatives. THROUGH our study, we found that companies are leveraging their data—AND SPECIFICALLY their customer contact data–in a number of ways.

YOU KNOW, it is important to keep in mind that successful businesses don’t just talk to their customers, they listen to them as well. TO THAT POINT, we found that 75% of U.S. organizations say that by 2020, the majority of sales decisions will be driven by customer data.

The chart on this slide shows why businesses maintain HIGH-QUALITY customer contact records. AT THE TOP, you can see that organizations maintain high-quality contact data to increase efficiency. NEXT you have a lot of areas of similar level, THINGS LIKE: achieving cost savings, enhancing customer satisfaction, protecting the organization’s reputation, and so on.

But, the CHALLENGE we see with customer data is that there’s so much of it. So in order to leverage this data SUCCESSFULLY, you’ll need to get better at MANAGING it … AND you need to TRUST it.

ERIN, can you talk a little more about this trust issue?

Thanks Sean. 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.

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.

In fact, nearly half of organizations globally say that a lack of confidence in data contributes to an increased threat of non-compliance and regulatory penalties. In addition, it also affects customer loyalty and the customer experience. If you don’t have confidence in your data, how can you even hope to have that more personalized customer experience or really know your consumer to make decisions at all.

Businesses today can’t afford to make decisions based on assumptions. If you business lacks confidence in the quality of your data, then there is a problem.

That’s right, Erin! And I think the point you’re trying to make is that the ONLY data worth having is trusted data.

SO….when you lack confidence in your information, it’s really important to get to the root of the issue.

We all like to think that all data is equal, but it’s not. There are A LOT of reasons why data can be untrustworthy. As you can see in the chart on this screen, we found that human error is the most common cause of inaccurate data at organizations. In fact, this has been the case for several years across our study. This particular question, however, was very interesting this year because we actually saw the prevalence of human error decrease by nearly 23% over the past year.

In my opinion, this tells us that organizations are getting better at EITHER training their workforce to use consistent data standards, OR that they have implemented adequate technology to prevent errors introduced by employees or customers at the point of capture. This can be things like address or email validation solutions that work in real time as information is entered. In general, we’re really encouraged to see this drop in 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. In our discussions with clients, we often 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. And all of this leads to an increase in human error and a high degree of distrust in information.

Erin, would you agree?

On average, 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.

If you remember on the last slide, you saw that 21% of organizations still say that inadequate support from senior management is a challenge. So with this high degree of distrust, why is that even an issue?

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.

Its time to start taking action around data and improving strategies.

I agree, Erin. It’s TIME to start ADDRESSING the root causes of data errors and building confidence in your data assets.

So how do you know if your DATA is TRUSTWORTHY? At Experian, we like to think of trustworthy data using the four pillars shown here. FIRST we have

Credibility: This is your data’s reputation. How do people in your organization or on your team talk about the quality of your data? Often this can have a big psychological effect on how much you trust your data.

Next, we have reliability: This is your first-hand experience with your data. Have your interactions with your data been largely positive, or have you gotten burned by inaccurate data in the past? Do you have automated quality checks in place (like those validation tools I mentioned before), or do you rely on manual processes to catch errors? If you rely on manual processes, it’s likely that you’re the one catching the errors – or, worse, they’re slipping through and leading to a poor customer experience.

The next pillar is transparency: The worst data is the bad data you don’t see. Do you have visibility into the quality of your data either by dashboards or regular reports? Even being able to see the uniqueness and completeness of values in your dataset can go a long way toward building confidence in the quality of your data. To do that, you’ll want a solution that can run full-volume analyses, not just samples of data. Further, being able to monitor your data over time will give you much more confidence in its quality and gauge the effectiveness of your governance around it.

We call the last pillar ‘Origination’: By this, we mean you should always know where your data came from and the circumstances under which it was created. Does your organization track the lineage as your data is standardized and transformed over time? Make decisions on data with unknown origins can be risky business, so lineage is key for confident decision-making.

If you are lacking confidence in your data, we recommend starting with these four pillars to identify where you could use some help. By excelling in each if these areas, you can achieve the last pillar --- yes, there’s a fifth pillar! Value. Trusted data will always deliver value to your organization. The definition of value can vary, but generally trusted data will deliver convenience, better products, better services, and so on. If your data is not delivering value to your organization, there is likely a lapse in one (or more) of these pillars.

To achieve that level of trust in data, you need quality information, AND you need a management process around data that gives you that level of trust. However, the challenge we find is that most businesses don’t have the right structure in place today.

Erin, can you review what we found in the study about the sophistication of data management strategies?

Thanks Sean. I think first we can say that there are a wide range of data management strategies out there. Here at Experian Data Quality, we define data management across four different levels, inactive, reactive, proactive and optimized based on people, processes and technology.

To give a quick overview of each of these areas, inactive is where there is little understanding of data quality impact, with reactive there are tactical fixes or tooling within individual departments, but few data-specific roles. Those that are around are probably sitting within the departments. In proactive, there are sponsors and specific success metrics, a clear ownership between the business and IT around data and a focus on data quality. Finally optimized is where there is central ownership of data, like under a CDO, data quality is monitored and part of standard operations and there is a platform approach to tooling that takes into account profiling, monitoring and even visualizing data.

In terms of where people fall on this maturity curve, just 18% of US organizations have an optimized data quality strategy. 26% say that they are proactive, which is up 2% from 2016. The biggest group is reactive, with 39%. This is a 4% decrease YOY. Lastly, 17% are inactive.

With that in mind, we looked at who has central control over data. This means that the data quality strategy will be more relevant and probably enforced more consistently.

Just one in four organizations say their data quality is reviewed and maintained by a central director, which actually declined 11% since 2015. Ideally, this number should be much higher in the future if organizations want to reach a more mature data quality strategy. The vast majority of organizations say there is some centralization, but many departments still adopt their own strategy.

To be fair, some of that could come down to the audience for this study. Individual departments feeling like they have more control over data than they actually do, but regardless it shows inconsistency in the management of data and a lack of a cohesive strategy.

While we find it encouraging that a majority of organizations do have data quality strategies in place, but the departmental silos in which the plans reside can lead to standardization issues and prevent data sharing across departments.

So which departments have the most influence when it comes to data?

The IT department is by far the most influential department, with 62% saying that they have the greatest influence on how data quality is handled. Today, most business users are making data requests to the IT department for generated reports or to help with strategic decisions. But there is frustration in this process and how it exists today. We found from the research that more than 70 percent of businesses have to wait a day or longer to receive their requested data from their IT department. That is a long time when you consider the real-time speed in which people are operating with today.

That is causing a bit of a battle between business users and IT groups. The study also found that that 70% of respondents also said that ongoing data quality should ultimately lie with the business, with occasional help from IT. Obviously that is different than who is controlling data today.

We think this is coming from a belief that data needs to be fit for purpose. Because the IT department may not always know the context under which certain data was created, nor do they know the intended uses for the data down the road, the business users are really the ones who can determine whether a dataset is good or not.

We believe there needs to be central control over the data for governance, security and standards, but the business users should ultimately have control to manipulate and use the data in the way that they want.

I think the last area we will cover is around the investment in data over the next year, which I will turn things over to Sean to cover off.

Thanks, Erin. The connection we see between the QUALITY of an organizations’ data and the TRUST they have in that data is undeniable. So we wanted to share with you all what we see organizations doing this year to improve their data quality. In our study, we found that more than 90% of the organizations say that they are planning a data project in 2017! And HERE is what they’re doing.

As you can see in this chart, data cleansing tops the list. I know I mentioned real-time validation tools before, but data cleaning can also be done in batch by running full lists of email or postal addresses or phone numbers to fix errors. We find that this is often a big help for companies that are experiencing issue with reaching their customers either for service or marketing. Going back to the focus on the customer experience we saw that beginning of today’s presentation, we think that this makes sense. And, under the lens of “trust”, knowing you have accurate data can help you rest assured your expensive campaign is reaching your customers’ mailboxes or you’re staying in compliance with regulation like TCPA.

The 2nd most popular project is data integration. Again, thinking about the issue of EFFICIENCY, we see businesses looking to combine data from disparate sources or siloed databases in order to develop information that is more meaningful to the business. You know, businesses HAVE a LOT of data. Sometimes it’s just really hard to pull it all together. By breaking down these siloes you can gain that TRANSPARENCY into your data we discussed before.

The next most popular projects we see are data migration, data preparation and data enrichment projects – followed by a wide range of projects. Only about 5% of organizations globally say they are NOT planning a data project this year.

As you can see in this chart, data cleansing tops the list. I know I mentioned real-time validation tools before, but data cleaning can also be done in batch by running full lists of email or postal addresses or phone numbers to fix errors. We find that this is often a big help for companies that are experiencing issue with reaching their customers either for service or marketing. Going back to the focus on the customer experience we saw that beginning of today’s presentation, we think that this makes sense. And, under the lens of “trust”, knowing you have accurate data can help you rest assured your expensive campaign is reaching your customers’ mailboxes or you’re staying in compliance with regulation like TCPA.

The 2nd most popular project is data integration. Again, thinking about the issue of EFFICIENCY, we see businesses looking to combine data from disparate sources or siloed databases in order to develop information that is more meaningful to the business. You know, businesses HAVE a LOT of data. Sometimes it’s just really hard to pull it all together. By breaking down these siloes you can gain that TRANSPARENCY into your data we discussed before.

The next most popular projects we see are data migration, data preparation and data enrichment projects – followed by a wide range of projects. Only about 5% of organizations globally say they are NOT planning a data project this year.

Other than more RESTFUL SLEEP, what will be the return on those investments? Our study found that a vast MAJORITY of organizations globally say that they have seen benefits across MANY AREAS of their businesses after implementing a DATA QUALITY program.

Our study revealed that 85 percent of organizations globally experienced more TIMELY and PERSONALIZED customer communications, as a result of improving their data quality solution. Another 83 percent of organizations globally say that they have seen some improvement in employee EFFICIENCY, and 82 percent say that they have seen some progress when it comes to REVENUE growth. If you RECALL, each of these were cited as TOP DRIVER for data use, so we’re pleased to see that their investment in data quality solutions is paying off. This means that organizations are TRULY receiving VALUE from their data quality programs – WHERE IT MATTERS MOST to them.

BESIDES just technology, businesses are investing in adding PEOPLE as well. More than HALF of the C-level executives we spoke to say that they plan to hire a Chief Data Officer (CDO) in the next 12 months. As Erin stressed the importance of a central data role earlier, we are really encouraged by that progress.

AND there are other roles coming this year! Companies plan to add data protection officers, analysts and compliance officers at more than A THIRD of organizations globally.

We see this focus on hiring DATA-RELATED roles as a step in the right direction when it comes to gaining TRUSTWORTHY data.

We all know that businesses today have A LOT of data, and the growing VOLUME and BREDTH of that information can make it difficult to manage. And that leads to diminishing trust. SO while we have all of this data, it’s actually really hard to use it for business purposes.

By looking at how good data powers business opportunities and why TRUST is essential in achieving those objectives, we can appreciate how important having confidence in your data really is. Remember, if your data is not delivering VALUE back to your business, it’s likely that there is a lapse of trust due to inaccurate data.

We also talked about some of the challenges facing organizations regarding their data accuracy, what modern data management practices look like, and what organizations are planning for the upcoming year. We found that while organizations still face a wide range of challenges and have a long way to go to reach that “optimized” level we talked about, their intentions to invest in the right TOOLS and especially the right PEOPLE tell us that they’re on the right track to building trustworthy data.

Sean & Erin

Now we’d like to open it up for any questions you might have. Please use the Q&A box on your screen to submit your questions. In the interest of logistics, I’ll read the questions you entered and Erin will be able to answer them.

So it doesn’t look like we have any other questions at this time. Erin and I would like to thank you for joining us today! For more data quality benchmarks, check out the full report on our website, edq.com.

And this concludes today’s webinar!