With the right data strategy, you can use data to impact the bottom line.
But what does it look like in practice?
Today, I'm going to show you how to design and implement a data strategy
that delivers business value back to your organization and we're going to use a six step data strategy framework to do it.
This framework has been created by IBM experts and used with our clients.
I'll also apply it to an industry specific example later on, but to put it simply,
the six steps are: listen, assess, apply, test, execute, and scale.
And the first step is understanding your business objectives by listening.
As most data leaders listen, they're asked to focus on one of three things, and it's typically a combination,
so increasing revenue, decrease risk, and improve operational efficiencies.
So talk to the C-suite about the top line strategy and develop relationships along the way.
Make connections for where data can be used to help achieve goals.
How digital transformation plays a role and note opportunities for alignment of your initiatives and business goals.
Step two is assess.
Look at the current state of your data use and data governance across your organization.
Review your notes and host deep design thinking sessions to really learn more.
The key is to foster an honest dialog about three key areas, so
the types of data being used in your organization,
the culture around data use within your organization,
and different operational workflows that use data, especially when we think about opportunities to do it better,
it'll give you that understanding of how customer, employee, operational, transactional and external data is
and could be used across functional areas of your organization, different LOBs [line of businesses], and use cases.
The next step is to apply what you've learned in steps one and two
and actually outline it in a document that is truly your data strategy for moving forward.
So it sort of line back to your business objectives, and definitely do not jump straight to a technology solution.
Take a moment, pause and include the people, processes and technology
needed to take advantage of all that data, AI, and automation has to offer your organization.
Step four focuses on testing your data strategy and refining it to establish those controls that are needed to be successful.
During these test scenarios,
look for areas in your data strategy that need to be refined to ensure data quality, data privacy, and data security.
Outlined findings with data and AI governance policy that actually helps protect your data.
These policies also empower data scientists and business users to work smarter with greater data access.
Now you can go ahead and prioritize and develop those small focus and MVPs [minimum viable products] and step five.
Now, these MVPs, as we think about them,
really need to go ahead and show progress in a short amount of time.
Then then iterate along the way.
Use these sprints to ensure that your MVP is aligned with business outcomes.
And test and refine the solution until it truly meets your user's needs.
These continual refinements encourage adoption, which is pivotal for the last step scale.
You can scale your solution to other functional areas, lines of business, and use cases that we mentioned earlier.
As you share the results of your solutions, you'll start to see a natural culture shift for your organization.
Stakeholders will begin to come to you asking to scale your results to their LOBs
as they see the value of and actually crave data-driven processes.
Let's put this into practice with an example.
Imagine you're a data leader at a bank.
Let's say it's a North American banking organization.
The biggest focus for your bank today is competing with
and differentiating yourself from the customer experiences offered by fintech [financial technology].
But how do you tackle this problem through a series of quick wins in a comprehensive data strategy?
As you listen to stakeholders in step one and assess the state of your current organization in step two,
you're likely to see data silos across lines of business that are leading to operational efficiencies.
So apply what you've learned in steps one and two to creating a data strategy that eliminates silos.
Keep those natural data connections between the lines of business.
Functional areas.
Use cases and teams in mind.
Ultimately, the entire plan should tie directly back to that enhanced customer experience
that you'll deliver through data accessibility efficiencies.
Now you can go ahead and test and refine the data strategy for KYC [know your customer] regulatory compliance and beyond.
The focus here is integrating client datasets across the organization without sacrificing data quality data
privacy and data security, and you'll do this through proper data sharing and permissions.
Next as you execute your strategy,
you start with that small focus MVP and let's say that we do the current mortgage underwriting process.
So this is an area with high ROI [return on investment] but a short enough production time,
you can show value quickly here. For example, in your first MVP, you not only integrate relevant data sets, but as you iterated,
you were also able to implement a virtual assistant
that quickly brings information that's relevant forward from each mortgage underwriter.
Since you picked that MVP focus that could be scaled to other lines of business area
and areas like let's say the credit card focus or, let's say, credit cards or customer service or sales.
You can actually use that solution to help further streamline customer experience across the organization.
As your solution demonstrates business value,
you'll organically grow that culture of data literacy, and that's what we alluded to earlier.
But that's a big factor in the success of your data strategy.
To learn more, check out the Data Differentiator IBM's Guide for Data Leaders.
Thanks so much for your time and don't forget to subscribe.