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#ModernDataMasters: Michele Chambers, AWS

michele chambers aws mdm

Kate Tickner, Reltio

michele chambers aws modern data masterMichele Chambers is a Leader of Product Management and Software Development at Amazon Web Services in Seattle. She has a proven track record in launching new lines of high-tech businesses (hardware and software) successfully and in creating visions and strategic plans for her teams to profitably execute upon. Michele is also a speaker, author and evangelist for the use of analytics to drive better business decision-making.

What was your route into technology, data and analytics?

I grew up in St. Croix in the Virgin Islands and was always very good at math. I loved solving puzzles – and my father encouraged me to challenge myself – but I didn’t have my first science class until we moved to the United States and I went to high-school. In school, I was fine if I could translate a science assignment into a formula or mathematics equation but I struggled with the language barrier at first. I soon got over that, though, and became much better at science with time.

I started out in research and development (R&D) in computer engineering and moved into large-scale applications development at Oracle,  where I cut my teeth on data and business intelligence (BI)-style analytics. The term “BI” had not been officially been coined as yet at that time, but there was a lot of summing, counting, and reporting nonetheless. I then worked on a large-scale BI project at Coca-Cola, which was where I really started to understand the importance of analytics.

I then moved to a consultancy that specialised in analytics, and deriving new insights and discoveries and formulating the data into actions. Our clients could automate these to continually improve their processes and their top-line, or make decisions that would significantly change the trajectory of their organisation. That company became part of Netezza, whereI saw the power of taking that type of analytics and putting it into large organisations at scale. That’s when things really start to take off in a business sense.

I wake every morning excited about the problems or challenges I am going to solve that day – it’s always been a pattern for me. My work life is so much fun because I’m still solving puzzles and having fun doing it just like I did as a child. I am very lucky to love what I do.

How would you define “modern” data management and the connection between that and business analytics?

To me, modern data management is about flexibility and fluidity. The earlier models made things somewhat rigid and difficult to adapt existing technology to a  business as it moved and evolved. Business moved faster than our ability to change the technology based on these relational database models. Now businesses are acquiring as much data as they can, trying to exploit it to drive innovation and new customer experiences.

You need flexibility and fluidity because the landscape is constantly shifting around you. That’s where cloud comes in because it provides the ultimate flexibility – as your business shifts, your infrastructure shifts easily with it.

Today, a business might need to store transactional data cost-effectively, and that’s no problem, but tomorrow you might need to store huge volumes of sensor data. Businesses  need for that to not be a problem either, and certainly not for it to be an 18-month project. They want to get there at a click of a button. At another time, that business might need cold storage for PII data for the last 20 years – you need to access it, but not pay for storage at a premium level.   

That’s what businesses are looking for – a big red easy button to help them change their data paradigm. For me, modern data management is about technology giving you the power to change the way your business is moving and with the same fluidity.

Advanced analytics, or artificial intelligence (AI), still has a ways to go. People don’t really understand it yet in terms of its application to business. They keep doing the same things and expecting different results – this is where there is a big gap.

“Many people tell you to start with the data and then think about what problems to solve – I think that is the tail wagging the dog.”

Start with the complex problems you want to solve in your business. Then look at the data to see if it is the right data to solve the problem at hand, or if you need to infuse it with new data sources to help derive new information. Or perhaps you need to synthesise the data based on your best guesses, allowing you to create simulations that can be evaluated and used to drive new value. This is something I don’t see many people doing in the market today. When it is done, the benefits are enormous – discovering new products, opening new markets, and deriving orders of magnitude in cost savings.

That’s what can happen when you leverage advanced analytics, AI, or business analytics – whatever you want to call it. You have to start with the business problem.

What are your top 3 tips or resources to share for aspiring modern data masters?

  1. Know what you are trying to solve and use the data to inform and shape your solution.
  2. Combine and use your data in new and different ways. If you keep doing the same things with the same data you will get the same results and everyone wants new results.
  3. You have to infuse your data with new data – use behavioural and operational data together for example – if you want new insights.

You have a lot of experience and success in product management in the data space– can you tell us about a time when you have engaged with business users to successfully drive new product innovations and what the outcomes were?

A number of years ago I did a project for that was all about harnessing AI. The company we did it for asked us to come up with recommendations about how to apply AI to their business. I did my research, learned about their business, and worked with them to uncover over a hundred possible solutions that would drive immense value for them. They were completely hooked because we worked out a rough-cut return on investment (ROI) based on interviews with their people about the main pain points in their value chain.

We asked, “What is it about your business that would be of value if you could know it today rather than in six months down the line?” We discovered all kinds of ways to use data and AI to solve all these really complex business problems.

Much of what people are doing today with AI are point-solutions – they are not about entire ecosystems. I always use supply-chain as the example of where advanced analytics and data have already been really successful, but nobody talks about it that way. Supply-chain is all about complex interrelated processes where decisions up-stream drive activities and consequences down-stream. You have to understand how they are connected so you can minimise or maximise down-stream impact appropriately.

What we did for this company was come up with a portfolio of solutions by understanding the entire ecosystem and how the point-solutions could work together. It all came out of applying business understanding to data and analytics, and they decided to go ahead with the top ten suggestions. That’s the piece that technical people forget – to join the dots between the analytics and its practical application in the business.

When Drew Conway coined the term “data science” he said you needed maths and computer science expertise – and there is a shortage of people with those skills – but the real gap is in the third area he identified which is domain expertise – understanding a business and its problems.

What trends or changes do you predict to the data management arena in the next few years?

The two I really see are:

  1. Edge-based cloud computing – Businesses will get to the point where they want really real-time results, requiring a shift to this type of system. You can think about this as a huge network where the cloud will be not merely centralised, but truly distributed right down to small environments. Even your local 7-11 store could rent out floor space for machines to their nearest cloud provider!
  2. Intelligent micro-services – This is so that people can create bigger and bigger intelligent applications.

Blockchain is also really exciting, and a basic component in edge-based computing. I think what blockchain is really about is creating a trusted network for sharing high-value information.

What would you say to young women about whether they should consider roles in technology or data management as a career and the opportunities available to them?

I always tell young women that if they love challenges or solving puzzles then there is lots of opportunity for them in this field.

‘You can go as far as you want to – there are no limitations – there are no barriers. The tech industry is broad and as you move through life you can adapt your career to where you are in your life at that time.”

This is a career where you can build deep friendships and your career can change and develop with you – you don’t have to leave it behind.

What do you like to do outside of work?

I have so many interests. I love art – especially art history – and to travel. I’m an avid reader, I knit, scuba dive, walk the dog, and lots more. I like to cook and eat too much – it’s very varied!

Which is your favourite fiction book, programme or film and why?

My absolute favourite movie is a Robin Williams movie called “Where Dreams May Come.” First off it stars Robin Williams, and he was a genius. But it is also a very visually interesting movie. It’s like a piece of art that merges surreal images with real life and it is very thought-provoking.

“It challenged me to get out of my comfort zone in terms of how I viewed life and death – and I like to be challenged.”

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