Radically changing consumer dynamics and the pace of technological change are fundamentally altering how companies today are leveraging data and analytics to enhance customer experience, mitigate risk, improve operational efficiency and increase revenue. Companies born in the digital era are using big data platform capabilities to flexibly access massive data sets in real-time to compete for market and wallet share.

Netflix famously spent $100 million on the first two seasons of House of Cards because they knew so much about the viewing habits of their customers and as a result, deduced that the show’s success was a safe bet.

In a transformation situation, when companies are seeking to make meaningful changes to tackle falling revenue or profitability, data analytics is still rarely front of mind for leaders at the outset. However, used as part of an overall transformation program, it is a set of tools that helps companies make fact-based decisions to improve their performance and regain a competitive edge.

“Analytics, RPA, ML and AI can play a role in tackling critical business challenges and opportunities.” says Darren Chapman, a Director with Alvarez & Marsal (A&M) in London. “The most successful companies are using data and analytics to answer the questions of what to sell and who to sell it to, and how to do that efficiently.”

“We’re not often called in because a company has a problem with its data,” says Justin Cooper, a Senior Director with A&M in London, who leads the firm’s European Data Analytics practice. “The issue is the business has a problem and they don’t know how to interrogate the data in order to solve the problem.”

Huge volumes of data can now be stored and accessed far more easily than even a few years ago. Certain sectors are particularly rich in points where customer or process information can be captured, such as from the machinery used in industrial manufacturing, or from inventory held by retailers. Yet many companies lack the ability to drill down into all this data to extract the insights that would help them deliver a different customer experience, for example, or decide where to introduce process automation or start using artificial intelligence.

“Not adequately defining the business problem, success criteria metrics or constraints of an analytics project” are common pitfalls,” says Mr. Chapman, adding that “Crisp-DM is still the top methodology for analytics, data mining or data science projects as it aligns the analytics to the business problem.”

Another key challenge is working with leadership teams to present the findings in a way that offers a plan for action, without seeming to point the finger of blame. “When we identify where a process is going wrong or where sales efforts are misdirected, it’s understandable that people feel they’re being told they don’t have a handle on everything they should do,” says Mr. Cooper. “To avoid this, we have to take people with us to show them analytics is not a black box, it’s reality, and make clear the value of what it shows us.”

Analytics tools can be particularly useful for companies which have grown through acquisition and ended up with a variety of different computer systems, meaning they have no straightforward way of comparing data from across the company. A&M has worked with clients to build solutions that pull data together in a standardized way to present a transparent picture of operating performance in every division.

There are several current regulations which require companies to have a clear picture of their customer data. European data privacy regulation across all sectors and worldwide “know your customer” legislation for financial services companies are a few example regulations where we are leveraging analytics capabilities to enable corporate compliance. A&M recently worked with an online gambling company to standardize its customer data and apply analytics tools, so that the company could oversee customer accounts in line with the requirements of its national regulators.

A&M’s analytics experts work alongside the project team at the client’s business during transformation projects, to ensure that data analytics tools are used to maximum effect. Throughout transformation programs, we ask all team members and client employees to suggest existing models and new tools that would give them a clearer understanding of how the business is operating.  At the end of the program, A&M trains people in the business to carry on using the analytics tools, or work with the company on an ongoing basis to update models and assess results.

“With analytics, companies can see for themselves how processes have improved as a result of the transformation work they’ve done – the metrics are there, they don’t need to take our word for it,” says Mr. Cooper.

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