5 Big Data Resolutions for the New Year

Instead of making the same old resolutions this year to eat healthier, drink less alcohol and exercise more, why not make a change in 2015 that will see drastic improvements to your personal and business success?

Here are my Top 5 New Year Resolutions to help you successfully tap into the wealth of data available to you to make better decisions, more often.

#1:  I will focus on value

It’s no secret that many big data projects fail to meet expectations, and in the vast majority of cases it’s because the project never had a targeted business problem or defined outcome at the onset.  Too often I see companies that want to leverage data in their business go to market for a “big data” tool, load all the data they can get their hands on into a mammoth mess, and expect magic to produce something of value.  It simply doesn’t work that way.

The most successful projects start with a defined business problem or opportunity, whether it’s to increase the success rate of marketing campaigns, identify employees are risk of leaving, or improve the accuracy of business forecasts for example.  Knowing the goal then drives the type and breadth of data that is required to derive a meaningful result, and that in turn influences the type of technology that can best support the data and analytical process, and ultimately produce a valuable business outcome.

No doubt many of you have well-formed thoughts about where you can get value from data in your business.  For those that are still looking for inspiration and ideas, here’s a few suggestions on how to get started:

  • Read case studies about what organisations are achieving with data and analytics – peer organisations in your industry, your clients, your business partners, even organisations in other industries that might face similar business challenges to your own.  ConAgra Mills famously looked to the aviation industry to find inspiration in how data can be used to optimise their business operations – sometimes finding a completely different perspective can spark a creative solution to your own challenges.  Here’s a great place to get started.
  • Brainstorm ideas with selected SMEs from across your business and the industry.  This means turning off technology, getting out the old white paper and/or whiteboard with coloured markers, and getting the creative juices flowing.  ASK:  What aspects of your business need improving?  What are your strategic growth areas?  What critical business decisions are you and your teams making based on gut feel?  How well do you know your customers?
  • Once you have your list of business challenges/opportunities you want to address, conduct a Cost-Benefit analysis to help prioritise your initial focus.  Grade each in terms of Time to Value, taking into consideration how easy/difficult it will be to get the specific data required to address that business challenge, and Value of Return to the business.

#2:  I will not let lack of perfection get in the way of progression

One of my biggest frustrations is watching a company spend years meticulously planning structured data models and integration points, only to finally produce a nugget of insight long after its value to the business has expired.  Thou shalt not let a lack of perfection get in the way of progression. You don’t need every data point cleansed, modeled and transformed to within an inch of its life in order to get started with big data analytics.  It all comes down to the cost of inaccuracy.  For example, if I said you could increase your marketing response rates by 20 points starting tomorrow, or you could improve it by 22 points but you had to wait 12 months to get started – which would you choose?  Often the data we have today is good enough to get a substantial improvement on business performance.  Unless of course your business objective is to reduce lives lost or another such critical business outcome where the cost of making a wrong decision is extremely high.

#3:  I will bring in the experts

I’ve worked with various information and analytics technology over the past 15 years, and it would come as no shock to you when I say, what I knew 15 years ago, doesn’t necessarily apply today.  Whilst technology in general has been able to scale to meet the sizing demands of today’s definition of “big data”, it has not always been successful in adapting to efficiently deal with unstructured data found in social media, case notes, call logs etc., nor to deal with the speed at which data changes and needs to be analysed and served up to a decision maker.  It’s hard enough for those of us that work for leading IT vendors to keep up with the rate of change in the industry, it’s even harder for those who have to do it on their own!  Bring in the expertise you need to make your first project a success, and make sure the sharing of that knowledge is an integral part of the project so your teams can be self-sufficient with applying big data analytics to subsequent business challenges.

Resolution #3.5:  I will not hire someone just because they call themselves a “Data Scientist”!  I’m not usually a pessimist, but from experience, there are many who put “Data Scientist” in their title the minute it became a buzz word and started drawing the big pay checks.  Do your research – hire someone who has a passion for data and analytics, and can work well with the business.  (And when you find them, don’t ever let them go!)

#4:  I will invest in a technology partner

There are some who still think the best way to invest in technology is to write a list of required features and functions, then peg vendor against vendor to find the individual products that together add up to 100% of the requirements list.  How exactly does that help anyone achieve a business outcome?  What you get is a list of ticks and a bunch of products that you or someone you pay has to make work, and no guarantees that when technology evolves (which it will inevitably do) it will still exist in the ecosystem.

Remember #1:  I will focus on value!

I have an ongoing joke with my clients that if they don’t want to invest in the technology, they can pay me a percentage of the increased revenue they get from implementing the analytics solution.  Rumour has it a US organisation tried this once, and months later begged to renegotiate their contract after they realised how much cheaper it was to just pay for the technology solution and pocket the profits themselves 🙂

Invest in a technology partner that makes your business success their priority – whether it’s a Global technology company, Systems Integrator, or local IT services group – one that is committed to seeing you achieve real business value from your investment, and that will guide you through the evolution of technology so you continue to see value over time. Better yet, invest in a technology partner that is driving the evolution so you’ll always be one step ahead of the game.

#5:  I will review and repeat

Once you’ve achieved #1-4 and have successfully completed your first big data project, the most important step is to review what worked and what didn’t work.  Did you meet the stated business outcome?  Did the technology partner you chose meet expectations?  Does your team have the required skills to leverage big data analytics for the next business challenge?  Develop a revised plan and move forward with addressing the next identified business challenge so you continue to get value from your initial investment.

 

These are just a few goals to set for yourself to successfully get started with big data and analytics in the New Year.  What other big data resolutions have you made for 2015?