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.

 

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Thats some good news ;). Having decided to make a career in this field, and still watching it find its firm feet, I think these occasional headlines are very importants for us to feel confident about the career choice we have made.

Analytics Start-Up Series – 2 of Many

I will continue with part 2 of the start up series and focus on the third part of the element wheel – New Product Design (Air)

New Product Design

What? By the time I had joined the team, we were still defining what we want to do, and what we don’t want to do. Analytics has come to connote not just data mining and predictive analytics, but even research analytics, product based analytics, dashboards, etc. We wanted to focus more on the marketing analytics, and that’s why we were MCoE.
However, the problem/uniqueness of our positioning was that we were focusing on a process driven analytics approach, while 90% of the listeners with different levels of analytics’ understanding had thought largely of product driven approach only. Even “SAS-skills” were “SAS” skills.

For whom? Moreover, we needed to decide which all verticals we will build our presence in. That’s tricky. Being small and new, you want to prove a point. You are ready for any project that comes your way. However, if you do your first project in healthcare with your key focus being FS, the next time you go to a client, you have a healthcare case study to talk about. If it’s an FS client, you neither want to own the case study, nor disown it. Boom!

Why? The bottomline for a sales guy remains – why would someone buy what you’re selling? Is there an identified need? Is it expressed? Would you need to educate the buyer? In the Indian market, for instance, Fractal Analytics, I think, has done a great job of educating the financial services sector about the need of and opportunities for analytics. There are similar examples elsewhere and in other industries too. Having said that, if we look at the analytics market today, the education is taken care of. The market does have an expressed need for analytics. If we take a closer look, the first industries to adopt analytics were financial services and telecom. And both these sectors loved keeping their data to themselves. They built strong in-house teams. However, things have changed in the last few years as firms have started engaging third party vendors (just as they adopted consulting/consultants as third party unbiased experts with a broader view) for analytics. But today, a lot of other sectors including Public Sector, Healthcare, etc. have emerged as buyers of analytics. Marketics, for instance, had more depth in FMCG than FS, given the ex-P&G background of its leadership team

Where? From a third party vendor point of view, almost everyone wants a piece of the US analytics market. The other markets have been slower to adopt analytics outsourcing. The other truth is the relatively crowded analytics vendor market in US. Net result, apart from some of the early movers like Fair Isaac, not a lot of vendors have been able to build a large scale. However, my MCoE stint taught me that there is a significant opportunity lying the Asian and European market as well, if you have the right connections, credibility and content.

Lessons learnt – Identify a market that you understand well, and where you have the credibility to sell. Sell only a bit, and understand it in depth, and avoid trying to be everything to everyone.

Analytics Start-Up Series – 1 of Many

Yesterday, I was talking to one of my friends who wanted to get my perspective on what did I learn or not learn about starting an analytics company (having been a part of multiple start-up environments). That’s when I thought tt might be a good idea to pen down my thoughts on this.
Disclaimer: This applies to my understanding of a company which is doing offshore analytics.

I always looked at my learning along four dimensions

This post is going to be the first in a series of posts where I will write about my experiences.

1. Strategic Alignment With Overall Business

I am drawing largely from my first company experience here, which was with a very large Indian IT firm thinking of setting up an analytics practice. When I joined the team, Gayatri Balaji was leading the initiative, and she had Dhiraj Narang helping her. Me and Shivani Sohal joined her as part of our third training stint. Apart from Gayatri, the three of us were raw with no analytics background/experience.

Analytics, or Marketing Center of Excellence (MCoE) was a prized initiative of the company at that point. It was an attempt to move up the value chain by doing “intelligent” work (in the Business Intelligence way, not that the company was not doing any intelligent work!).
However, that being said, what our small and inexperienced team (with the exception of Gayatri) soon realized is that its one thing to say that we want to do “this”, and another to align it to the overall business.

There are four set of challenges that we discovered –

A. Existing product portfolio

Context : The company already had a BI practice, a CRM practice, and a lot of analytics was already being done in different relationship pockets. To put it bluntly, every time a relationship needed someone with “SAS skills”, they hired one and put him/her on the relationship. No need to aggregate the “SAS skills”, (which is what analytics job postings have reduced the required analytics skill-sets to!). Additionally, tools like SAP and Oracle have their own analytically intelligent layers, and SAP and Oracle are separate practices within the organization. Imagine your plight when you’re talking to a client about analytics and she says – “Well! But that’s pretty similar to what you’re BI team talked about. They probably had a higher product focus, though!” And you start looking at the account manager, who has probably introduced every practice to the client (to grow the account). It was not our fault that we were the new baby on the block. Interestingly, the leadership had never thought of aggregating the knowledge lying here and there in the firm to have a solid ground from the beginning.

Lesson Learnt: If you’re going to cannibalize your existing product line, you need to be sure on what you are offering, why you are offering, and how will you work with the existing product lines.

B. Stakeholder Alignment

Context: In the organizational power play – strategic positioning can mean that you are the weakest (fresh out of the closet) player, or the strongest player (the whole company is looking at you). Usually, you are not stuck in the middle. If you are the strongest, the performance becomes extremely short term oriented. Stakeholders want to see quick wins, proof of concepts and a latent potential (as visible through a practice bursting at the seams!)

a) “Who’s with you?” – We realized pretty soon that very few important players have been sold the concept and its importance to the overall soon. The attitude towards the new practice ranged from “This is neat!” to “Oh! So we are wasting money on analytics this time”. To an extent, the varying levels of cynicism is expected in large organizations. The problem we faced where cynicism in the mind of decision makers/policy makers.
b) “Who gets the credit?” – Driving from my other experiences, analytics team can potentially be at direct conflict/synergy with another practice. For example, an offshore analytics center (analytics outsourcing) model can be a potential threat as well as support to analytics consulting. Analytics (platform independent) can be a threat to product driven analytics, but can be used to augment the nature of analytics as well.
c) “Who gets the money?” – Given that analytics is a horizontal solution and not specific to industry, revenue recognition is always a challenge. All the verticals stake claim to the analytics revenue, while analytics unit may have a separate revenue target. For instance, a 100MM target for Financial Services vertical will be achieved through products, services, analytics, implementation, etc. However, the 20MM analytics target will be achieved through a combination of work done across verticals- such as Financial Services, healthcare. Every dollar generated by analytics team will be claimed by the respective vertical. However, the effort devoted to sell analytics will be lower, because there is no specific FS-Analytics revenue target.

Lesson Learnt: If you’re an outsider roped in to run a new business initiative, make sure that you understand the powerplay and relative buy-in. Understand the weak and strong points of everyone you’re compulsorily going to deal with. Equally important is to understand the relative aspirations that help you share, transfer credit of the work done in a politically correct manner. Sales is a tricky issue that we will touch separately later

(…to be continued)