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Data is business

Organizations have been saying for several years that data is a critical asset to achieve any number of objectives, including customer experience and revenue growth. Data should now be a part of all business operations and decisions. But how many organizations are actually taking that mantra and leveraging data effectively?

Listen to our recent webinar, featuring Forrester Principal Analyst Michele Goetz, as we talk about how organizations can improve data management techniques to more effectively leverage their data assets. We cite new Forrester and Experian research studies to drill into:

  • Overcoming data aspiration challenges
  • Evolving data governance trends
  • Forming a collaborative data partnership
  • Gaining intelligence around your data

Thank you so much for joining us today. My name is Sean Coombs and I will be your moderator for today’s webinar.

 

I am a big fan of the topic for today’s webinar. Organizations have been saying for several years that data is a critical asset to achieve any number of objectives, including customer experience and revenue growth. Data should now be a critical part of all business operations and decisions. But many organizations are struggling to make that mantra a reality.

 

Today we are going to talk about how to overcome some of that challenges and leverage your data more effectively.

 

To do that, I am joined by my colleague Erin Haselkorn, our head of market research at Experian, and Michele Goetz. Michele is a principal analyst at Forrester serving enterprise architects, chief data officers, and business analysts trying to navigate the complexities of data while running an insight-driven business. Her research covers artificial intelligence technologies and consultancies, semantic technology, data management strategy, data governance, and data integration.

 

Thank you both for joining me.

 

Here is our agenda of what we will be moving through today.

 

Michele will cover off the challenges with data organizations are seeing today, how you can follow the data to allow it to tell a story and how to set priorities when you understand what is going on and need to make improvements.

 

Then Erin is going to cover off some trends in data investment around technology and people and then then tips for how you build a business case for improvements.

 

We will be taking questions at the end, but feel free to enter them in the chat or question box throughout the presentation.

 

With that, I will turn things over to Michele to get us started.

 

In order to make data a more trusted and accessible business asset, and therefore a part of business operations, we need to improve the management of data.

 

As Michele spoke about before, data is essential for digital business and our research shows that using data can provide some key strategic competitive advantages.

 

From a global research study we conducted this year of over 1000 individuals from 4 countries, respondents said that they leverage data to improve their relationships with customers, to gain better insight for decision-making and also for more efficient business practices.

 

So what are organizations doing to gain this competitive advantage?

 

98% of companies have a data management project planned in the next 12 months. Actually the number of respondents saying they are planning projects increased by nearly 10% YOY. Again, as Michele said, data needs to become a standard part of the culture, but there are certain initiatives that need to be done in order to reach that goal.

 

In terms of the projects, the purple line was the responses from last year and the blue line is for this year. Some of the main focus this year is on data integration and analytics. Both of these appear to be driven by that easy-to-use mantra and putting data in the hands of the business users. You need to gain insight and integrate certain subject areas or sources, but again, that needs to be built around the needs of the business.

 

Data migration is also up there as businesses move to new systems to handle the digital demands and away from legacy technology. You also see Governance as a focus for 34% of respondents, but I do believe that is higher based on some of the conversations I am having this year.

 

Again, all of these should be designed to help businesses move to a more data-driven culture and find trust in data so they can leverage it. Right now, we have tons of data, but are frankly short on meaningful insight.

 

With all of these projects, we need to consider multiple facets. You need to think about the people, the processes and the technology. The people around data management that can decide which areas to take on and how to best utilize data within the business. That is a very hot area for 2018 and a major priority, specifically, who needs to run these data management initiatives.

 

Unclear ownership of data and a lack of authority are compounding the data-related challenges many organizations face. The graph on the screen shows where data ownership sits today. This is not too surprising as the legacy ways of managing data depend heavily on IT resources.

 

It is interesting to see a quarter of respondents saying the CEO, that goes up to 31% for the US. I think this is an example how data has achieved asset-class within organizations today. You also see the CIO mentioned so another c-level title with a fair amount of ownership.

 

There are also a fair number who say ownership is divided between departments. This could be problematic when cross-departmental communication is cited as a primary source of data inaccuracies. Then you see the rest divided between data governance and the chief data officer, which is rising and we will get into that a bit more later.

 

While IT owns the data now, there are a fair number of problems with the current structure. As Michele said, we can’t seem to leverage the data we have and there are major problems, despite the fact that we all realize the importance of it.

 

So where should it sit? While many argue that IT owns the stewardship and storage of data assets, the volumes of data and the shift to cloud-based storage services are changing this model. There is the question of where things are today, but there is also a question around who should own the data. More and more, organizations are asserting that business users should own the data. Now that is a big change from where it is today. They have the subject matter expertise to understand the context around data creation and, therefore, to know when and how it should be analyzed.

 

Ultimately, it is our believe that data ownership should sit with the business. And the c-suite agrees. 91% of c-level executives say that they believe the responsibility for data should ultimately lie within the business with occasional help from IT. In addition, 90% of respondents who work in IT agree that the business should retain responsibility for data programs.

 

After you think about the people, there are also a number of trends around technology. To execute on those areas I highlighted earlier, you need to also leverage technology. However, what we need out of our data management technology is changing.

 

We see that 98% of businesses are planning to buy data management technology in the next year.

 

Here are some of the top factors they are looking at when choosing technology. Ease of use is number one which we have talked about. That is because the user of these tools is changing. It is moving more towards the business, which means they may not have the same skills and training as traditional IT resources. In addition, we are all facing a huge skills shortage and while some business can afford to pay tons of money for huge numbers of data staff, others can’t. To fill in the gap of that skills shortage, some are relying on smarter and easier to use technology.

 

You can also see that general features and functions are high, as well as reporting capabilities.

 

I was surprised that time to value was so low. To me, this is very important, especially for business users. However, maybe some of these businesses are not having to justify these purchases quickly and show a return on investment.

 

Given these initiatives around data, how can you get started? How can you gain interest within your business and drive investment for these projects?

 

A successful business case for data quality should contain quantifiable evidence. If you’re stuck, ask yourself the following questions:

  1. 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?
  2. 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.
  3. 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.
  4. 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.
  5. 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.

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?

 

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.

 

At this point, I will turn it over to Sean for the Q&A.