Step-by-step: preparing your business for big data

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Step-by-step: preparing your business for big data

[Blog post] David Gee offers his practical tips.

Big data. It’s the hype-fuelled juggernaut that few CIOs can afford to ignore for any longer. But jumping on the bandwagon is not going to be easy. Here are some practical ways to make sure your organisation is ready to make the most of the deluge.

Consider a study tour

Alan Grogan, partner at Advanced Capability Solutions in London, says that letting third parties in on your big data plans can be vital “as this will help you later on if you need their buy-in and support with a project”.

But he adds a cautionary note.

“IT alone won’t be able to help you with commercialisation of your data into information and then business knowledge,” he said.  For this you will have to do some personal study and network heavily to get detail on user cases both in your sector and across other industries.

One way to accelerate this learning process is to seek out some expert guidance. An option is to undertake a ‘field trip’ to a handful of leading-edge analytics organisations based out of India and/or the USA.

Sri Annaswamy, founder at advisory firm Swamy & Associates, endorses this approach

“There are a number of options, with varying degrees of usefulness. You can buy a standard industry analyst report (Gartner, HF etc.) or a white-paper from a standard consulting firm (KPMG, Deloitte etc.) But neither option would provide the ‘hands on’ learning associated with being on an analytics factory floor during a field trip” he said.

Another learning to pursue is getting an idea of what an onshore and offshore analytics centre of excellence will feel like and indeed how this could work for your own enterprise. There is nothing like seeing an offshore centre in action and getting a sense for the talent pool that is in place.

The degree to which your business will consider ‘partnering versus owning’ is a key question. But certainly does not have to be answered immediately. This can definitely wait as you and your team learn by observation and also by osmosis. 

Learn by outsourcing

Ok, so you want to learn more about this strategic topic – big data.

But you’re also aware that this skillset is in short supply and that no-one wants to expose their thinking or share information at this stage.

For those of us that are true-believers but lack the requisite deep experience, then the best way to learn is to consider outsourcing big data functions. From my own experience, I used this approach many years ago in another domain as I was establishing a customer contact centre for Asia Pacific.

This may sound counter intuitive, but it is actually the fastest way to learn what best practice looks like. From this position you can then decide whether you want to grow your capability internally or continue to outsource.

There is a saying that when the processes are a mess then you will be outsourcing a mess. It is true, but you can also learn from working with great partners.

Picking my partner?

For those of us that have been around a few seasons there are some common sense criteria for selecting a suitable partner. In essence you want to be buying high quality data analysts who have done this before in industries that you rate as being leaders of this new category.

Grogan has narrowed his advice down to four tips:

  1. For your specialist areas and projects you should have dedicated – ideally named – people who support only your business. This is not just to ensure analysts who ‘get’ your business, but also to protect any intellectual property.
  2. Be careful that the vendor lets you exclusively own any new processes they develop for you. This may sound obvious, but I’ve heard of companies effectively signing away their business models.
  3. Be careful of vendors using very fancy phrases, promising the latest technologies and overstating their capabilities. Be mindful that it’s known for consultancy and technology companies to ‘wash’ their capabilities with other more basic capabilities, and then re-outsource the difficult stuff. 
  4. Contact any potential vendor’s current big data clients and ask how their needs and wants have been supported.

Who should ‘own’ big data?

As a CIO, should big data even be your responsibility?

Alan Grogan says the way to approach this question is to forget about ‘ownership’ and ‘territory’.  For big data to succeed there needs to be a strong focus on the customer and an understanding that the culture of the organisation will evolve with this transformation.

“Big data ideally needs to be sponsored by a CEO (local, regional or global) who is the custodian of culture.

“In terms of driving the agenda, the owning technology area (CTO/CDO/CIO) should really engage with each business area and function that stands to benefit. Over time, as big data processes become more embedded in the organisation, it’s not unimaginable that ownership will split between functional leaders (head of product, head of sales, head of finance) etc.”

He recommends that appointing a joint sponsor is the best approach, within the function or area which will fund the project. It’s critically important that this strategically positioned executive straddles the entire organisation and reports to the CEO.

Self-service philosophy

Big data demands self-service from day one. This is what will drive success. It is a long way from the traditional data warehouse and data mart approach and IT will have to evolve to work within this paradigm.

Let’s remember that the single-most important purpose of big data and analytics in an organisation is to enable end-users to go directly to the data source and carry out the analysis and visualisation they require directly, without relying on an IT-department-based BI and reporting team to prepare reports for them.

This approach shouldn't result in a lack of governance but it is radically different from how it was approached in the past. In effect, it means those of us in IT will have to let go of a little control.

One proof-of-concept is not enough

Cloud options have lowered the barriers to making a start with big data. But it is also important not to rush into anything that hasn’t been thoroughly tested.

The concept of testing big data through a proof of concept is clearly the recommended path.

In fact Annaswamy recommends enterprises don’t just run one POC “but three to five POCs in parallel, utilising different potential strategic analytics partners”.

He explained that this “helps accelerate proof of the value of big data and analytics to the executive and in a tangible manner as well as uncovering the specific capabilities of different service-providers in a defined timeframe and cost bound manner”.

What changes to the organisation are required?

One can argue that big data is naturally already the job of the CIO or CTO – so why would you hire a chief data officer or chief analytics officer?  I remember the rise and decline of chief knowledge officers that have come and since gone from the ranks of corporations.

My advice is: let’s not let this happen again, so IT, including the CIO, have to embrace this change.

There is still a need for an appropriate framework – with end-to-end data stewardship to drive data quality and make sure all data falls within a governance, risk and compliance framework. The most important thing is to get executives to grasp the importance of big data and analytics for every part of the company’s business.

Business gap analysis

A gap analysis is not a business case. Instead it is designed to illustrate how real the gap between your organisation and the industry leader(s) is – and how to bridge that gap.

Annaswamy maintains that the length of the review depends on the type of analytics project. In the case of a simple cross-sell uplift or an attrition problem project then it could be within 12 months.

However if it this is a complete revamp of your analytics platform, to say an enterprise data warehouse with the incorporation of a Hadoop-based system for unstructured data plus visualisation and newer software, you could be looking at a three-plus year scenario.

Copy cat – who should I benchmark?

Benchmarking against peers is the trickiest aspect because anyone that is doing this well is not going to share their intimate details.

Recently I was speaking to a CIO in Singapore who worked in an industry completely removed from my base in financial services. Despite my best attempts I could not convince him to divulge big data specifics.

But one hint is to look for organisations that are customer focussed, have made significant investments in this area and have appointed chief data or analytics officers.

In Australia it is understood that Suncorp and CBA are the most advanced in this space, but watch out for Walmart, AIG, Amazon, Google and Pay Pal. 

Reskilling my team

I’ve been really curious about what skills are really needed to manage an outsourced big data capability. The most important part is managing the diversity of staff – standard IT developers, programmers and BI techies won’t do!

You will need engineers with strong statistical-modelling experience, behavioural scientists to pick out the insights and operations people to plug the insights into specific interventions.

There is an assumption of course that vendor management today is a given. Most organisations have been through the IT offshoring/BPO cycle for several years now that they have reasonably sophisticated procurement and vendor management teams.

Reinforcing this view was a further insight from Grogan that relationship and listening skills are all key. The biggest challenge is simply getting the outsourced capability stood up. 

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David Gee
David Gee is an accomplished consulting and technology executive who has held CIO roles in Australia, China, Japan and the US. He explores the role of the CIO and the transformation journey. Other favourite topics include digital innovation, analytics and big data and the financial tech ecosystem.
Read more from this blog: G Note

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