Skip to main content

Scaling data and analytics

Driving transformation and decision making

Catch up on Experience Analytics: The Virtual Series - watch the episodes you missed or re-watch your favourites!

How often has the outcome of a brilliant piece of analytics ended up in a set of slides lying on your desk? A challenge for most organisations is making the insight available systematically at the point of need. In this blog, I’ll explore how scaling data and analytics can transform how your organisation makes decisions. Let’s begin…by looking at some examples:

  • A sales rep gets a list of prioritised opportunities to go after each day
  • Retail store manager gets alerts as soon as stock of an item is below expected levels on the shelf
  • Dynamic pricing on store shelfs
  • And the list goes on….

Often companies have analytics teams that heavily focus on innovation – let’s build some cool models or at the other extreme – initiatives focus on getting the data right before experimenting with advanced analytics, artificial intelligence (AI) and machine learning (ML).

Scaling analytics requires a shift in approach:

  • Skillset: A combination of business, data science, data engineering
  • Mind-set: A culture of innovation and industrialisation and process change
  • Partnering: The new IP, not intellectual property but rather inclusive partnering with business and IT

Many are still of the opinion that analytics is one area that cannot be outsourced and they try to create this capability in-house, which is yet another barrier to scaling the change that can be brought about by data and analytics.

It is almost impossible to have an in-house team achieve scale:

  • Talent: Analytics talent is not easily available and competitive pay by the tech giants makes retention a problem
  • Capability: Specialist capability is required, driven by fast-paced innovation. For one initiative you may need an natural languages processing expert, and for the next, you may need someone with data engineering or visualisation skills or Python skills
  • Size – not enough people to cover the breadth of activity needed from data, insight, AI, technology and change

Recruiting a large team to do all of this means a massive operational expense budget. So the exam question is – who should build this capability vs. who should find a partner to make the most of data, analytics and AI?

For some companies, investing in a large in-house analytics capability makes a lot of sense. If we look at some of the tech giants, they are successful because of their ground breaking search capability, which is at the cornerstone of their business. Keeping this capability in-house and protecting intellectual property is the right thing to do.


What is the business of a retailer? To sell products

What is the business of a Consumer Packaged Goods client? To manufacture products

So does it really make sense to spend the same amount time and resources in developing an analytics capability in-house, compared to the tech giants?

For most companies, a combination of an in-house capability augmented by an analytics partner is the right answer to scaling their analytics capability. This will drive results most efficiently and cost-effectively.

Working with an outsourced analytics partner doesn’t quite mean lifting and shifting the entire function to your partner or vendor like you would for IT. Rather you look for a partner who you can closely collaborate with, someone who can help you with the following dimensions:

  • A partnership model which allows for flexibility while bringing in the right kind of skills when required
  • The ability to appreciate and understand your business,
  • Brings you the best in class products and accelerators so you don’t need to start from scratch every time
  • Has the expertise to deliver against any business requirement and equally has can drive innovation cost effectively (Push vs. Pull).

In addition to finding a partner, organising for success and driving a data-driven culture are equally important aspects of scaling your analytics capability.

What next?

How has your organisation embedded and scaled the analytics capability? Do post your success stories in response to this blog post. I look forward to this discussion!

Sign up for the latest updates