Should Data Analytics Be Outsourced?

I was following this discussion on linkedin, and as expected most of the responses fall in the extremely simple to understand category of – “it depends”.  Because, fact of the matter is, like everything else, it depends.

So, I am trying to move from my usual “it depends” to the hopefully helpful “it depends on”.

1. Is Analytics at the core of your business? (Like it is at Amazon or Capital One. Think about how you make decisions in your strategic and tactical reviews. Think about how most important discussions take place in your organization. )
2. Do you have a leadership role equivalent of Chief Data Scientist in your organization? (This could be Chief Analytics Office, or Head of Risk Analytics, or Head of Consumer Insights or something like that. And hopefully, this is not just a figure head designation. )
3. Do you have a well-defined analytics function? (How big and organized is the analytics team of your organization? Do you have one large team? Or many small teams?)
4. Has your analytics function grown rapidly in the last 2-3 years? (Think about the number of people or the variety of projects handled by your analytics team)
5. Do you have a set of senior people who can scope and define analytics projects? (Managers/Senior Managers who can add business layer to analytics problems (art) while having enough understanding and appreciation of the tools and techniques (science))
6. Would you say that the organization has mature data assets? (Do you think almost all your potential ideas and hypotheses in the recent times have been addressed without there being significant data gaps or assumptions to be taken care of? Are these optimized databases? Are consistent and quality dashboards available across the organization?)
7. Do you have any existing outsourcing relationships already? (Say,  data warehouse? Or, HRMS? Or some MIS reporting?)

 

The more the number of yeses that you score between questions 2 through 7, the higher your chances of being able to extract good value out of a outsourced data analytics. For 1, if your answer is yes, you should NOT outsource your data analytics.  And for the discussion here, outsource references to long term outsourcing contracts, and not  one-off analytics projects given to external vendors/ consultants.

 

1. Is Analytics at the core of your business?

Like most things core to your business, if analytics is core to your business, then you should not outsource (you can still offshore it to your own captive center, though that would be a separate long debate). Risks include exposing your core to 33 other through offshore employee churn, significant management bandwidth wastage in protecting core IP, and not getting enough business input from the outsourced relationship. (Most risk analytics firms in the market have their founders coming from strong risk backgrounds at banks and investment firms. Did they violate some NDA or IP act when they set up these analytics firms? There is a very thin line to be de drawn here between what exists in one’s head as knowledge and what exists on a piece of paper as protected IP). Outsourcing as a process serves you well when you focus on repeatable non-core activities where efficiency/ costs/ speed/ organizational focus, etc.

 

2. Do you have a leadership role equivalent of Chief Data Scientist

This is clear outcome of your focus on analytics as a differentiation capability and your ability to dedicate senior bandwidth to an outsourced analytics relationship. Most technology outsourcing contracts have had senior CIOs/CTOs paying close. So, if you want to make these work, better have a senior guy look at it. Not the brand management fella (no offence meant).

 

3. Do you have a well-defined analytics function?

Ah. So, you don’t have an analytics function at all. Which more often than not implies that you haven’t thought through what exactly is the analytics you need or what for do you need this analytics. In this case, I recommend that you give one off projects to someone through your IT team, which most likely owns the data at this stage.

 

4. Has your analytics function grown rapidly

Usually, this would imply that the buyer base has grown/diversified. It gives you an opportunity to organize, consolidate and focus on the more strategic projects being taken care of internally or through one off projects, and a lot of ongoing reports/ standardized work being executed through an outsourced relationship. This is the time where you start measuring efficiencies in your current activities.

 

5. Do you have a set of senior people who can scope

In most cases, the project scope can vary, and data definitions need to evolve on a constant basis to meet the needs of the business. Unless there is someone senior enough who is paying attention to these, the ball does get eventually dropped and you hear comments like – “but the last time…” or “but I thought…”. Such involvement also ensures that the outputs are not devoid of business relevance.  A committed senior person ensures relevant utilization, better output and better value for the organization.

 

6. Would you say that the organization has mature data assets?

Unless your data is reasonably mature, there is no point pursuing analytics, leave alone the idea of outsourcing analytics. Any step to your organization using analytics starts with using data, which starts with cleaning data. This was a fun question to catch the slackers!

 

7. Do you have any existing outsourcing relationships?

Unless you know how to handle outsourcing relationships in general, you should not think of analytics outsourcing. Howsoever standardized the analytics might become, its more knowledge intensive and artsy than system intensive. Hence, you should rather focus on system or process intensive work getting outsourced first, understand engagement and communication protocols, SLAs, dealing with outsourced cultural conflicts, etc. before you outsource knowledge intensive activities.  And an embedded assumption here is that standardize reporting contracts are “process intensive” and not knowledge intensive.

 

The answer to the above questions usually gets you closer to “CAN analytics be “SUCCESSFULLY OUTSOURCED”. Whether it SHOULD, is still going to be a wider debate, which includes cost benefit analysis, availability of talent for captive organization, scale efficiencies, IP protection, doing it offshore with your own unit vs. doing it outside, etc.

 

Summary Of the LinkedIn Comments –

The arguments for outsourcing included – skill set gaps in the organization, cost arbitrage, focusing resources on core activities.

Arguments against include – always worthwhile to build own analytics capabilities, QA issues, turnover in vendor and subsequent knowledge retention and transition, engagement protocols and batch mode vs. interactive real time mode of working, and lack of business knowledge/context knowledge.

 

Some of the comments (in summary, not verbatim) are included here –

• Gary S – No one knows business better than company employees.  If they have the analytical skills, then analytics should not be outsourced.
• Simon G – It’s a worthwhile investment to build in house capabilities, even if it takes time. .. Sometimes a particularly specialist and repeatable requirement comes along, which is suitable for outsourcing.
• Peter W – Sometimes an outsider can spot something missed internally.
• Michael Mout – Depends on the size of the company. Smaller companies cannot afford a full time analytical team.
• G. Jack Theurer – Difference between outsourcing and offshoring (maybe to an internal unit). Outsourcing lcaolly has benefit of specialized knowledge and talent, and occasionally budget. Offshoring – difficult to run in batch mode. DA is an art and a science. Science is the same across most organizations. Art isn’t.
• Nagesh P – Skillset gap can be filled by the external team
• Kapil M – is cost arbitrage still there? Cost vs. Value?  80:20 model (extended team vs core team) – meets the business knowledge needs as well
• Duane S– You can train your staff rather than trying to outsource.
• Jon Jian-An L – business analytical problems are always undefined and do not have closed form solutions. Issue of high analyst turnover – retention of informal knowledge
• Mario Segal – Turnover. Challenge of smooth transition,  strong on analytical techniques but weak on products or markets.
• Krishna Agarwal  – Is the core product/service based on analytics? If so, keep it inhouse. Most organizations do not have senior mgmt bandwidth/ devote resources for creation of the ecosystem needed to realize the full potential, ROIof in house team is genrerall y poor.
• Imran Ahmad Rana –Studied Analytics in Quality. Orgs that have it inhouse are more effective than those who are outsourcing.