What Are Your Organisations Aspirations For Data?

What Are Your Organisations Aspirations For Data?

What Are Your Organisations Aspirations For Data?


It’s been about three weeks or so since I attended CogX, with its excitement and somewhat hysteria around Artificial Intelligence (AI).  Since then I’ve been lamenting about AI, Machine Learning and all the snazzy things that I heard.  The pinnacle of my attendance was seeing RoboThespain!  An actor robot with some fast and furious humour, which could only have been so good as I’m sure there was another person listening and seeing everything we were doing and responding in haste.  No robot as yet, has the ability to be so quick to quip and think like a human.  I’m definitely sure of that, otherwise Karel Čapek and Isaac Asimov would have jumped out of their graves!

Coming back to my thoughts, and to the first few sentences.  We seem to be going through waves of hype at the moment, and the waves are getting stronger and bigger.  I’ve now been working in the area of data for almost 20 years! Wow, can it be that long! So, within that period, there has been a lot of talk about strategies, technologies and transformations.  These “ies” and “tions” have been wide and varied from ERP to CRM and now to Digital.  In large, implementing these technologies failed, due to the very nature of focusing on technology and not thinking about how these technologies would support users, partners, suppliers and customers.  I think we are living in the same realm, and people are fooling themselves, if they think they won’t make the same mistakes with digital transformation or AI as they did with the other technologies.

While I want to speak about the nuances of AI, I actually want to talk about the fundamentals of AI.  What do you think the fundamental of AI are?  Well, I happened upon this fantastic quote (which I love), by Gerardo Salandra, Chairman of the AI Society of Hong Kong and CEO at Rocketbots:

“Artificial Intelligence is your rocket, but data is the fuel. You can have the best algorithms in the world, an amazing rocket, but you’re only going to get as far as your data gets you. Data is fundamental — data is AI.

Wow that excites me, and so succinctly put, I wish I had said it!  So true, data is “fundamental” and yet so overlooked by many organisations today.  Data is the key enabler, fuel, and will only continue to persist as organisations generate more and more of it!  This is an article that focuses on the output that is generated from all of those systems and what companies do with “data”.

Where is all this data coming from?

Let’s look at where data can come from today.  It comes from the traditional customer transactions and enterprise systems such as CRM, ERP, HR etc.  It streams at speed from your weblogs and identifies how your customers interact with your company website and ad platforms.  We are moving to a connected society, so “things” are now generating more data than ever (only to explode exponentially).  Coming from sensors, connected cars, homes, transport, or otherwise referred to as the Internet of Things, Internet of Everything, the Internet of Customers and Smart cities.

It comes from all your social media channels, 3rd party sources such as demographic data.  Open data sources are growing too, with a lot of data from the public sector, such as: environment, mapping, crime and justice, health, transport, education and so on.  The list is endless, and data is only going to keep growing in greater volumes, and become more advanced.  Data needs to be seen as a critical corporate asset. 

With Data Comes Great Responsibility

As with the title of this article, companies are going to have to think deeper about their aspirations for data.  Why?  Because, as with any resource that persists, there are a number of challenges that come with it.  In this case those are:

  • Lack of skills – the demand for skills such as data scientists is now increasing into the hundreds of thousands and worldwide, we could be looking at a bigger cumulative issue. Attracting and retaining talent will be one of the biggest wars that organisations will face.  The Silicon Valley companies are hoarding the best talent, so I think there will be a massive issue now and in the future.  The data scientist in my opinion is old news now, still very much required, but companies need to be and think more diverse in this area.  I was having the conversation with another data colleague the other day and we discussed the good old “hybrid”.  The person that can combine data insights with functional knowledge, which will power the business.  These types are a dime a dozen right now, and organisations have to tap into their knowledge pool to dig these types out, and leverage their talents before hitting the ivy leagues.
  • Who’s on first base – the Chief Data Officer is the next resource that is steadily rising, but, I must remain cautious here as there is a lot of jockeying happening in the C-Suite. They seem to be hitting the shelves, and a lot of companies are appointing, but not authorising CDOs to get the job done.  Gartner predicts that 50% of appointed CDO’s fail in their journey.  So, there is still plenty of work to be done and challenges remain as to whom they should be reporting to, and how long they are needed.  Some people suggest that this is a transitory role and not needed for the foreseeable future.  I say humbug! I’m blowing raspberries right now!
  • What’s it saying my precious, the precious will be ours – like the ring (Lord of the Rings) many organisations are lured, no hypnotised by technology. Many are still heavily investing in technology first, thinking the tech will be able to drive their ability to gain insights and look into the future.  Technology is purely an enabler and will never be the remedy!  It is part of the eco-system but needs to feature once you know what you want to do.  There have been many organisations that I have worked with, that had to retire investments, for one reason or another. Don’t let it be another vanity project, and from my perspective, that’s why data must be prised away from technical hands.

Data opportunities are still being overlooked

An organisations ability to leverage data will effectively become their competitive advantage.  But, organisations are still stumbling, and few are pursuing data strategies.  Those that are the Amazons, Ubers, Netflix, Googles etc. will continue to disrupt the market, and companies will fall behind or die.  These companies today are wholly focused on disruption through their data and analytics assets, and strategies. Investing in your new digital platforms, data and the required talent, will be the differentiator moving forward, there is no doubt about that.

A survey by Econsultancy issued in 2017, stated that “62% of businesses have no data & analytics strategy.”  That’s just in the area of marketing, so you can imagine if this is one department, just think how bad it is across the enterprise where there is no real enterprise data strategy.

Whether it be AI, Machine Learning, IoT, Smart Cities and so on.  All of these applications, tools, technologies, algorithms, either need data as an input to train a model in the case of AI / Machine Learning.  Will generate vast amounts of data from sensors and connected things, needing both data inputs and outputs, to be computed at the edge and to run algorithms in split seconds for recommendations etc.  At the heart of these is the beat of data, running through their veins, waiting for answers and decisions at a fast and furious pace – to either a human or a system.

How Do You Compete Then?

If you don’t have a data and analytics strategy in place to understand how you will map the needs and assign value to the business, by associating it to the plethora of data available, building a value roadmap to generate the required insights to drive your strategic objectives, and finally get your data architectures in place to support this.  Then there is no point in even thinking of future related objectives such as Artificial Intelligence.

Thinking of data as a by-product of an application will not suffice in this day and age.  Data needs to be managed as an asset and needs to be treated with care, like the envelopes that you get your degree certificates in, those are never creased, they are stored in a safe place and used to provide the insights and credibility for you when asked.  So, treat data in the same way you would treat your kids or your pet – love it and nurture it.  Back to your aspirations as a company.

What does data and analytics strategy cover?  The basics (not an exhaustive list, but covering the main principles):

Step 1: Undertake a review the current state of the data landscape

  • What does the journey look like thus far
  • What is your level of maturity towards data as an organisation
  • What requirements have been gathered from the stakeholders
  • What tech is in place and how is it supporting / adding value to the business
  • Have people change the way they work and think about data
  • What are the current challenges with data, these could be definitions, quality etc.
  • Who are the people that control, manage, and exploit data assets? What does this organisation look like?
  • Basically, a full review of people, process, technology and culture around data

Step 2: Where does the business want to go with data now and in the future

  • Creating the right vision for data and analytics so that people can be aligned and bought-in
  • Mapping out what the business wants to achieve (key business questions the business is asking themselves daily but can’t seem to get the answers to)
  • Aligning requirements to the organisational strategic objectives and layering in the decisions that need to be made to reach those objectives (this portion adds to the “why” we are doing this and culture),
  • Creating an understanding of how the requirements will be tackled i.e. dashboards vs. machine learning algorithms (this uncovers a menu of items that can be matched to technology and skills)
  • Establishing the priorities for the key business questions (this is what the business thinks are the priorities)
  • Analysing the data that exists to see what business questions can be answered. This gives the real priority as you can only work on the requirements where there is available data
  • Creating the data value roadmap which provides the practical and logical sequencing of data initiatives
  • Defining what the future operating model looks like
  • Designing and understanding the data governance and data architecture that will need to support all data initiatives
  • Working out what’s required to start the operationalisation of data
  • Assessing the skills gap and articulating what is required to fulfil the future requirements of the business
  • Assessing different models of competency centres to create focused data teams on the horizon

**I would further add that throughout this process you will be working with many different stakeholders who will all have different needs.  Working with the change team (if there is one), is going to be paramount to start the thinking around how a successful data programme will affect the culture.  Then to put interventions and plans in place, to ensure that while the data element is marching forward, the people aren’t being held back.  Don’t underestimate this area, it’s precious to overlook, and is, in my opinion one of the biggest impediments to any kind of “tion” in an organisation.  It would warrant a whole article to itself.**

Step 3: The final straight

  • Documenting all the final recommendations dotting the “i”s and crossing the “t”s.
  • Presenting the future roadmap, articulating quick wins and costs to start executing the strategy and getting sign-off to go forth and datify the organisation.

Step 4: Put your feet up, only for a moment though, your competitors are sniping at your heels

  • Have a quick break and a kit-kat and start your data revolution!

Begin executing your strategy and live up to your organisations data aspirations!

Success Comes To Those Who Dare And Act

The keys to success are to determine how your business should respond.  I would add to the list above, that you need to assign clear accountabilities / responsibilities for data strategy and results, and then move ahead to execute the needed changes in a systematic and logical fashion.

I compare having a data and analytics strategy, and casting my mind back to the 90s, of having an internet strategy. Those that did have an internet strategy conquered the web, those that didn’t and ignored it like Blockbuster, failed and are no longer with us.

It’s an exciting time in the world of data, so create your data and analytics strategy, get your foundation in place and reach for the stars.  You will be pleased you did.