Updated: Apr 28, 2024
We were invited to join leaders in Digital Transformation for a conversation on building business success with data. During this Fireside Chat, Fuse Data CEO, Dave Findlay touched on three areas for leaders to improve business with better decision making.
To help you pull some usable details, we’ve laid it out for you in these articles:
Working with Data Leadership
Listen in to the full Fireside Chat here
Have you seen things change over the last decade in how data is used to improve business?
The biggest thing that's changed is the business perspective on data and analytics. Ten years ago, it was a back office, IT thing. Historically, if I needed some information for a quarterly financial report or sales report, I would email the person over in IT, they would write a query and pull it.
Now, Executives are depending on the vertical, more data driven. They want to be more hands on. They want to understand what their leading KPIs and trailing KPIs are. They're much more data driven. They don't want to make decisions based on their gut anymore. So I think that's definitely an attitude that I've seen change over the last five to 10 years.
Today, we are getting much more aware of the need to be problem focused instead of technology focused.
Do you think business leaders are becoming more data savvy as technologies are much more accessible to people at a younger age?
I think that's definitely one of the things why we're seeing Executives become more and more data savvy, data driven is because, there are people being promoted up into these positions, new CIOs, VPs of IT or, or COOs and they're expecting more and more technology to be in place that helps them do their job.
Even at the individual contributor level, people are expecting the same. For example, if in a sales profession, they don't want to roll-a-dex, they want some technology that helps understand things like, “what are the right customers to target or who's exhibiting buying signals?” They want to be assisted in that capacity.
The other thing you see with technology is the boom of SaaS technologies. From an enterprise perspective is interesting too, because typically IT would own everything. If you wanted to install a new piece of technology, you would go to your IT department, and they would source it, or they would build it in a lot of cases. A lot of companies had big internal software development functions.
"Now with SaaS, any department in your organisation can stand up an enterprise grade platform. So you have a proliferation of technology throughout the enterprise, which is great, because the business can just go stand up what they need and get the job done. Now it creates a data problem, because you have all these systems, all producing their own data sets, not necessarily all in sync". - Dave Findlay on Shadow IT and Data System Silos
If a manager wants to be more involved with KRIs and KPIs to improve decision making, how would you assess that?
So, you could look at how the analysts would do it. Gartner has an analytical maturity model and you can plot a company against that framework. There's the DAMA wheel and data management framework where you go through the wedges of that wheel and assess where people are in each aspect of data management.
What I like to do is borrow from both of those things. I really like to take my message around people first so I'll look at how people are doing their job. That helps me understand how an organisation is, or where they're at in terms of analytical maturity.
So if I go into an organisation and I shadow a sales team or a maintenance team, and observe them pulling data from five different systems, and see them cleansing data (because this system uses this abbreviation, this system uses that abbreviation) I know okay, they've got a master data management problem.
If they're cleaning up sales figures they might have a data quality problem or application integration problem. If you take that grassroots approach, along with some of these other more established frameworks, you start to get a real understanding of where the organisation's at and an understanding that's based on empathy for the end user.
The people that are on the floor doing the job, the ones that need the information day in and day out. I think if you have that, you have a much richer sense of where companies are at in terms of their data and analytics maturity.
How do you typically find companies react to your data team joining theirs to co-create?
When we go in, we're definitely there to help people. We’re usually brought in because there's a challenge and that challenge is felt on both sides of the aisle. When we go in and work with technology teams that are established, they are very capable people that are on top of things, but there's just something not working right.
They're usually very happy to have some assistance and someone that's on their side speaks their language and is able to help them bridge the gap - The gap that usually exists between the technology team and the business team within the customers that we serve.
Have you had any a-ha moments with your customers?
I would say the biggest thing I encounter is, companies all have this desire to do more with data. They all understand they need to be data driven, but it gets lost in the, “how do we go about doing that?”
There's this frequently cited statistic where
lots of industry analysts will say things like 75% of technology initiatives fail to deliver positive ROI and that's a crazy statistic if you think about it. The big learning here, the a-ha moment is when companies are at a point where they realise the old way of doing things, the old way of approaching technology initiatives needs to change because something's broken and we can't just keep doing the same old thing.
