Session Information:

Transforming Through Analytics in a non Technical World

Why do some transformations work and others don't? Experiences leveraging analytics across multiple industries to do transformation in companies.

This session will explore examples and situations where transformation has succeeded or failed with an emphasis on common themes from the speakers direct experiences. Participants will have an opportunity to hear:

• How to think about the life-cycle of a transformation program
• How to focus resources and energy on different aspects of the effort to get optimal outcomes with an emphasis on value creation and sustenance of efforts

Session Transcript:

Hello there um I'm Rajeeve Kaul, I'm here to talk about cso cxo perspectives and technology and business transformation. It's a topic that's near and dear to my heart so before I get into the details let me give you a little bit about my background and how I got here so historically I have had experiences across multiple organizations industries started out my early career in the analytics space built capabilities around the application of machine learning optional intelligence and so on in multiple companies set up centers of excellences ran data warehousing and technology groups moved in the last several years into pricing and p l responsibility type roles in 14 primary companies.

Again my current role is as an officer with global responsibilities with McDonald's so that's me in a nutshell what I'm here to talk about with you today is uh transformation and really the tension between analytics business and some things that I've learned as i've gone on this transformation journey some of the experiences and some of the learnings that i've had my point of view as i talk about this is going to be very much business focused and i'm going to talk about some of the blind spots that i've observed that hopefully you're going to fall into some of those traps.

If you can vicariously learn from me so let's talk about the first one first topic I'm going to focus on is the aligning of data strategy with business strategy this is really really critical to do upfront if you want your transformation effort to work let me give you a story a brief story of a experience i had with a large corporation that i was in an executive leadership role with there's a new cio in town let's just say there's a town hall for all the i.t and analytics folks and a big part of the transformation overall for this business.

That the cio is talking about is the agenda the digital agenda and its role in making sure the systems and the capabilities are built out and we can capture all the data and know everything about the customer it's a new it website and an app the entire team is turned around a business that's struggling and we will be able to grow our business through this digital transformation it is investing a ton of money in this situation um they will create loyalty platforms.

That'll support growth this has been communicated externally to the street and through the investor community that this omni channel strategy will be the savior unfortunately the first thing that people didn't look at as we went down this path was we're dealing with very deep legacy systems and legacy systems have constraints in this case they didn't have the ability to capture or even execute targeted offers.

So you can invest all you want in this technology but unless you change the guts out of the basic transaction systems of the company you really can't execute the strategy right so what i want to call out to you is that the entire conversation starts with goals of the business goals are one part defining smart goals is difficult defining a goal is pretty easy but goals need to be connected to business strategy and business strategies then will lead to some strategies and tactics in themselves and it's the tactics and the work within the tactics that has to get executed to deliver on the goal um in order to do this if efficiently you need a decent work plan the work plan you need to look at it in a consulting speak from a mesi point of view right.

It has to be items that need to be done that are complete that are necessary that are sufficient analytics in this entire conversation is typically going to be a support function analytics is about making sure that the decisions are being supported with the facts that are being required to deliver these strategies that are the ones that are then helping us achieve the goal and it's not the front end of this and the mistake that we made was in that particular story we started with the technology we started with the analytics and we got ahead of ourselves not realizing that to achieve the goals you have to connect all of these different pieces together.

So my first lesson is about the precursors um i would suggest to you that investing in analytics is it's a little bit sometimes like taking up an exercise program when you transform through data when you transform through analytics which is the in thing nowadays to talk about and focus on sometimes it may take time to see these benefits i'll give you an analytics uh story.

I worked with a very large fortune 500 automotive retailer and the the gentleman responsible for the systems was very forward thinking in the 80s this person had put in place the capability to be able to track a change in an item number in the system in multiple retail and warehousing distribution businesses that i worked this is one of the fundamental flaws.

That you find in a transactional system in these retail and distribution businesses that when you switch an item number the history gets lost so longitudinal analysis is just not enabled in the core process when you try to then figure out what happened two years ago the same item numbers can be repurposed today it might be a battery yesterday it might mean a break the day before it might mean something else so if you haven't put in that investment upfront which in this case was put in in the 80s they put this in the ability to capture that historic transactional insight.

Then when the need arises you can't address it without a massive redo of your systems and processes so think about investing in some of these capabilities as a little bit like taking up an exercise program the results may not be immediately evident but in the long run it's definitely what what you get paid out from another set of experiences.

I want to talk about is from the perspective of centralizing IT data and analytics functions I've been done this story several times before the key hypothesis that I've noticed when you talk about centralization is efficiency.

We'll reduce cost capabilities we'll have a team of experts now in one place that can provide much better capabilities to us that can enhance our effectiveness we'll have good governance much better insights so let me give you an example of a story.

I experienced and participated in in a large distribution fortune 50 company we had a we had a business where each business had its own resources and capabilities doing analytics there was a proposal that was put forward to centralize this analytics function um bring everybody into headquarters we spent about an year trying to get alignment right and we thought we had everything worked out and then we ran to all kinds of headaches and as we fell into these situations we went but i thought we had covered this ground so my my thinking here is that when you enlarge corporations in large businesses and centralization is the theme of the day keep in mind.

That corporate functions want to centralize they want to centralize because there is a fundamental belief at headquarters often that folks in the region in the far-out offices don't know what they're doing they're not capable once we can get control we'll be able to do so much better whenever you see this hypothesis playing out ask yourself where is that data where are the facts because in many cases there are incomplete facts and a lot more perceptions and if you don't follow the data the next leadership will still be struggling trying to complete this journey and unsuccessfully whenever you go down this path of centralizing in a transformation just make sure.

That you get alignment early you get alignment often you get alignment and realignment through the process and then get some more alignment across all the stakeholder groups and foremost remember change is not something you do to someone change is something you do together with others a common story another very common story I hear is whenever you talk about centralization regions distributed operations capabilities the story.

I hear is the issue is them it's the other department i could do better if we could start it all over again we would have done it like this or i would have done it like this be careful when you hear this remember people always have agendas so be careful and vary if you're making commitments moving forward be ready to walk the walk and always be real when talking about change change does not happen without a lot of effort and if you want to know how well your change could succeed in your transformation look back into the history in that company look back into history for evidence to see.

Where are the examples that have seen success in this business on transformation and then what are the fundamental truths that made that transformation successful that'll teach you a lot um another story i hear often is around integration uh integrating systems uh getting the new technology moving to the cloud better insight i want to talk to you about a business that i worked with razor thin margins very large company multiple stakeholders all talking about the fact that look systems are old antiquated don't support our future business the call to action was that.

Hey we're seeing failures where we cannot even print invoices sometimes in time so you know if you got a business and you can't even print out your invoices then you got a problem right you can supply all the product you want but you can't collect the money so you're kind of dead so the discussion was often about systems being archaic systems about to fail in that story the curveball was our competitors are transforming.

So we are being left behind our competitors are transforming we are being left behind and unfortunately if we don't transform we will be will be gone so if you look at that kind of a story a fundamental belief in that story is that if we can integrate systems across multiple parts of our business we can give much better outcomes the the question is what are the assumptions behind that story ask yourself that integration will make information available well are the base core systems capturing that information that you want available through integration decisions will be better and outcomes will be different that's one of the integration mantras well.

If decisions will be better and outcomes are to be different who is making those decisions is it the same people is it different people do they understand how to do it differently ask yourself what are you encountering are you encountering the bias inherent in people in these situations or are you encountering a complete story based on facts that'll help you figure out a little better which way to move one of the problems i've noticed is that often the transformation efforts new capability acquisition efforts often they are led by the staff functions so marketing corporate marketing strategy those are the kind of people that will be behind transformational efforts in reality you want the operating heads to be behind the transformation the people.

Who are in the line functions that are responsible for delivering the results are the ones that should be the ones that need to be in the forefront of making sure that things that are needed are actually the ones that are being delivered ask yourself where does the buck stop in your company and where the buck stops is where that leadership should be coming from not some other place remember data and analytics are at one end of the spectrum from data analytics and better tools you will be able to find yourself on a journey to going to a better skill set.

So data analytics tools skill set and then looking at outcomes differently a willingness to challenge assumptions and ability and willingness to change processes all of these have to come into place before you can get a different outcome a better different outcome doesn't happen because you got better data better tools better skill sets all of those are necessary but they are not sufficient for a better outcome a better outcome requires a very different mindset that you have people in to work in reality always remember people have agendas those are always going to be real in reality data is foundational bigger databases more complex databases will not necessarily lead to better outcome they will lead to a lot more cost but it is our ability to take that data and get to an outcome that you've got to probe on and understand.

which of those components of that capability exist before you start throwing a ton of money at this and don't forget the goal is not better data or better analytics and transformation the goal is a better business outcome which is measured for most companies in terms of an impact to a p l or a balance sheet or free cash flow statement of some sort so that's what you're chasing and focus on the right issues remember people who have been successful widely successful in their careers without data suddenly won't adopt the mantra of data and become data serums.

They will need to figure out how to operate differently with data they never had and make decisions differently so don't underestimate the effort of changing behavior that has been built up over decades in an organization bottom line keep in mind at the end of each spending bonanza in a business is a bill that comes due at some point and that bill has to be paid the question is who will be paying the bill once the bonanza stops.

Thank you for listening to me hopefully you enjoyed the talk happy to take any questions but appreciate again the time you.