In an earlier blog post, I wrote about the importance of data governance and agile development for digital transformation. In this post, I’m going to talk about another aspect of data governance: data analytics.
In their new role as innovators and strategic advisors, CIOs need to broaden their focus beyond provisioning applications and KPIs. One of the things they need to pay attention to now is data itself. Why? Because getting the right data to the right people at the right place and time is critical to business transformation.
Few companies will be able invest in data to the degree that Amazon, Google and Netflix have, but those companies provide examples of just what can be achieved when companies collect, analyze and leverage customer data effectively. Whether it’s making product recommendations on Amazon, delivering relevant ads on Google, or recommending the right movie to a particular customer on Netflix, data and data analytics provide a key competitive differentiator for all these companies.
Organizations that don’t make the most of customer data and analytics are going to fall behind significantly. Executive teams know this, and they’re putting pressure on chief information officers and chief data officers to make sure they’re leveraging data effectively.
Let’s talk about how CIOs should respond.
Find out three steps that CIOs can take to drive digital transformation — read the Boomi executive brief on “Why Digital Transformation Projects Fail.”
Data Analytics Requires Data Integration and Data Governance
You can’t do data analytics right without data integration and data governance.
Data integration makes data available for analysis, and data governance provides “guard rails” in terms of the practices and the technologies that people use to ensure data is safe and structured.
CIOs should think of data governance as the way to open doors to the right data sets with the right structures for getting data to the right people. This way, more employees, partners and customers can leverage the organization’s data sets and use them the right way.
Data governance tools include data catalogs, which help people know what data exists in their environment and who the subject matter experts are. Data governance policies ensure that people are accessing the right data and using that data the right way.
The combination of data access and data governance helps organizations answer questions such as:
- What does revenue mean across all our applications?
- What do certain dates mean in a given data environment?
- Where are the data dictionaries for defining these terms?
If you start building analytics without a parallel effort in data governance, you are likely to run into problems. You’ll start basing analysis on incomplete or inconsistent data.
You may also run into the problem of having too many dashboards, too many reports, and inconsistent end user experiences. Without a governance framework in place, people have a tendency to keep creating new dashboards and reports for special purposes. This can generate conflicting definitions of key pieces of data. A better approach is to define standards and reapply what’s been created previously.
One of the benefits of data governance is that it prevents a sprawl of data and analytic assets. This then protects against the misuse of data that hurt productivity.
Getting Started With Data Analytics
How can a CIO go about setting effective systems for data integration and data analytics? When I work with CIOs, I take two approaches. One is top-down, and the other is bottom-up.
The top-down approach is to start asking questions about the things people should know about. These might be very rudimentary things such as:
- What are the strategic goals and how are they being tracked today?
- How do you define markets, customer segments, and customers? What insights are you lacking that inhibit serving customers optimally or limit opportunities to grow business?
- What are the leading indicators that a customer is at risk or may be open to buying additional product or services?
- Where are we spending money where there is opportunity to drive efficiencies or automate?
- Which advertising channels attract the most loyal, profitable customers?
- Which marketing tactics are bringing in the most new customers?
In many organizations today, people have difficulty answering strategic questions like these, because they don’t have access to the data sets and analytics necessary for answering them.
And here’s something else to explore: What’s the value of the answers to these questions? In other words, if you knew the answers to these questions, what would you do next? How do you make these answers actionable? What would you do differently in your daily work and in the organization’s overall strategy?
In my practice at StarCIO where we help organizations drive smarter, faster, innovative business transformation, I use the results of this brainstorming to figure out what things the organization should focus on and prioritize.
Bottom-Up Analytics Assessment
The second approach, which looks at the problem from the bottom up, is about looking at the data assets that already exist in the organization. You should catalog what those assets are, what state they are in, who owns them and why they exist. There’s a lot of value is putting together a basic inventory of what data assets the organization already has.
Next steps will vary, depending on the state of the organization’s data.
If the organization has a broad set of data, their data sources haven’t been integrated, they have poor or incomplete data dictionaries, or there are few people in the organization who understand the data dictionaries, that’s a clear indication a lot more data governance is needed.
To address this, an organization needs to focus on setting up dictionaries, conduct data profiling and perform data quality analysis.
If the organization has a lot of very sensitive data, but there are few data policies in place, you might have to spend some time drafting policies. You’ll need to come up with some data security requirements. Keep in mind not only your business goals but also new regulations such as the European Union (EU) General Data Protection Regulation (GDPR).
Are you still struggling with GDPR compliance? Boomi can help. Read the executive brief, “How Boomi Can Help Your Organization Respond to the GDPR.”
You’ll want to have those policies ready before you start tapping into your data sources to access data for analytics and other business purposes.
On the other hand, if the data sources are relatively easy to work with (low-volume and structured), then it might be best to put some data visualization and discovery tools in place.
Explore what people could do to answer questions on their own, using self-service portals and the like. This approach will also help you unearth any underlying data quality and governance issues.
The CIO might end up leading all three types of efforts — writing data dictionaries’ data governance, crafting data policies and looking at self-service. It really depends on the specific needs and goals of the organization.
Fast and Flexible Data Analytics
In all three cases, you’re going to want to work with IT systems that make data integration fast and easy. Most importantly, these data systems must make data governance easy, rather than becoming another obstacle to productivity.
Rapid, low-code development is useful here, so that data access and data analytics can be implemented quickly, giving the organization the data it needs to gain a better understanding of its operations, make better decisions and get on with the important but challenging work of digital transformation.
But a CIO can’t lead an organization through digital transformation without the intelligence that comes from data analytics. And you can’t have good analytics without good data integration and data governance.
To learn how Boomi’s unified integration platform can help you drive digital transformation within your organization, contact a Boomi integration expert today.