Analytics: Insource or Outsource?

For someone who makes their living from consulting on analytics my answer to this question may surprise some. In a world increasingly dominated by data, the ability to leverage data is not only a source of competitive advantage it is now a required competency for most businesses.

External consulting can help accelerate the journey to fully insourced analytics capability. The trick is how to do this in the most cost effective way. I have dealt with a number of companies that have very different approaches to this question, and it is my observation that the wrong mix of insourcing and outsourcing can be very expensive, perhaps in ways that you may find surprising. The key is understanding that analytics is not primarily a technology function.

To illustrate my point I am going to describe the analytics journey of three hypothetical companies. Our three companies are all challenger brands, second or third in their respective markets. Their businesses have always been reliant on data and smart people, but new technology and competitive pressures mean that data is becoming more and more important to their business models. All recognise the need to invest, but which is the right strategy?

The CIO of Company A has launched a major project to implement a new ERP system which will transform the way they will manage and access data right across the organisation. He is also establishing an analytics team by hiring a handful of statistics PhDs to extract maximum value from the new data platform. He is investing significantly with a major ERP platform vendor and is using consultants to advise him on implementation and help manage the vendor. He sees no need to spend additional money on analytics consultants because he has already hired plenty of smart people who can help him in the short term. He does however see value in hiring consultants to help his organisation with the large IT transformation.

In Company B, the COO is driving the analytics strategy. Privately, he doesn’t rate the CIO. He sees him as coming from a bygone era where IT is a support function to the core business and technical capability should be delivered from technical centre of excellence. The CIO has built a team of senior managers who firmly believe that to maintain efficient use of resources; business users should only have access to data through IT-approved or IT-built applications. The company has a very large and well organised data warehouse, but mostly it is accessed by other applications. There are very few human users of the data, and virtually none outside of IT who mostly use the data warehouse for building applications and rely on a detailed specification process from internal “customers” to understand the content of the data.

To drive his strategy of developing organisational analytics capability, the COO is forced to either wait for lengthy testing of new applications and system access through an exception basis, or else outsource his analytics to service providers who can offer him greater flexibility and responsiveness. He secures funding for an asset management project to optimize spending on maintaining ageing infrastructure and secures the services of a data-hosting service. Separately, he hires consultants to build advanced asset failure predictive models based on the large volumes of data in his externally hosted data mart.

Company C has hired a new CIO who has a varied background in both technology and business-related positions. She has joined the company from a role as CEO of a technology company where she has had both technology and commercial experience. Her previous company frequently (but not always) used Agile development methodology. She too has been tasked with developing a data strategy in her new role. Company C is losing market share to competitors and the executive think this is because their two competitors have spent a large amount of money on IT infrastructure renewal and have effectively bought market share by doing so. Company C is not using their data effectively to price their products and develop product features to drive greater customer value, but they are constrained in the amount of money they can spend to renew their own data infrastructure. The parent company will not invest in large IT expenditure when margins and market share are falling. The CIO resists pressure from the executive and external vendors to implement a new cut price ERP system and instead focuses her team on building better relationships with business users, especially in the pricing and product teams. She develops a team of technology-savvy senior managers with functional expertise in pricing and product development, rather than IT managers. She delivers a strong and consistent message that their organisation’s goal is to compete on data and analytics. Every solution should be able to state how data and analytics are used.

As issues or manager-driven initiatives arise she funds small project teams comprising IT, business and some involvement of external consultants. She insists that her managers hire consultants to work on site as part of virtual teams with company staff. Typically consultants are only engaged a few weeks at a time, but there may be a number of projects running simultaneously. Where infrastructure or organised data does not exist, teams are permitted to build their own “proof of concept” solutions which are supported by the teams themselves rather than IT. Because the ageing data warehouse struggles to cope with increased traffic increasingly it is used as a data staging area with teams running their own purpose built databases.

So how might these strategies play out? Let’s look at our three companies 12 months later.

Company A has built a test environment for their ERP system fairly quickly. The consultants have worked well with the vendor to get a “vanilla” system up and running but the project is now running into delays due to integration with legacy systems and problems handling increasing size of data. The CIO’s consultants are warning of significant blow outs in time and cost, but they are so far down the path now that pulling out is not an option. The only option is to keep requesting more funds. The blame game is starting with the vendor blaming the consultants, the consultants blaming IT.  Meanwhile the CIOs PhD-qualified analytics team have little work to do as they wait many months for their data requests to be filled. The wait is due in part to the number of resources required to support the ERP project means that there are few staff available to support ad hoc requests. When the stats team gets data they build interesting and robust statistical models but struggle to understand relevance to the business. One senior analyst has already left and others will most likely follow. I have seen this happen more times than I care to remember. Sadly, Company A is a pretty typical example.

Company B has successfully built their asset management system which is best in class due to the specialised skills provided by the data hosting vendor and analytics consultants. It has not been cheap – but they will not spend as much as Company A eventually will to get their solution in place. The main issue is that no one is Company B really understands the solution and more time and money will be required to bring the solution in house with some expenditure still required by IT and the development of a support team. On the bright side, however, the CIO has been shown up as recalcitrant and the migration of the project in house will be a good first project for the incoming CIO when the current CIO retires in a few months. It will encourage IT to develop new IP and new ways of working with the business including sharing of data and system development environments.

Company C (as you may already have guessed) is the outstanding success. Within a few weeks they had their first analytics pricing solution in place. A few weeks after that, tests were showing both increased profitability and market share within the small test group of customers who were chosen to receive new pricing. The business case for second stage roll out was a no brainer and funding will be used to move the required part of the data warehouse into the cloud.

After 12 months a few of the projects did not produce great results and these were quietly dropped. Because these were small projects costs were contained and importantly the team became better at picking winners over time. Small incremental losses were seen as part of the development process. A strategy of running a large number of concurrent projects was a strain at first for an IT group which was more accustomed to “big bang” projects, but the payoff was that risks were spread. While some projects failed other succeeded. Budgets were easier to manage because this was delegated to individual project teams and the types of cost blow outs experienced by Company A were avoided.

The salient lesson here is to look firstly at how your organisation structures it approach to data and analytics projects. Only then should you consider how to use and manage outsourced talent. The overarching goal should be to bring analytics in house because that’s really where it belongs.

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