Big Data Analytics is changing the decision-making structure of organisations

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Big Data is changing how organisations make decisions, who makes the decisions and even the types of decisions to be made. If we think of organisations in terms of decision-making structures and processes of decision-making then what will these look like in an organisation that fully uses Big Data analytics?

Some people are well past the ‘bigness’ and IT focus of the Big Data conservation and onto the Analytical Strategy (e.g. McKinsey or Booze & Co). But I haven’t seen much about how decision-making in organisations will change and how this will change organisational structures and activities.

But the issue of how Big data Analytics changes decision-making has cropped up recently in my dealing with several organisations and it could completely change in the next few years. Here I’ll explore some of the options and possibilities for organisations and their decision-makers.

Centralised versus decentralised decision-making

Lots of firms with huge numbers of staff in different locations have a highly centralised way of making decisions. Take the major retailers. Most of them have moved to a highly centralised ordering policy – so centralised that store managers are not in charge of ordering stock for their own store. Store managers handle staff and service but they can only indirectly influence the orders that are delivered to their store.

I once asked senior managers from one major UK retailer if it would be a good idea to give some decision-making powers back to store managers. The reaction was shock, a waving of hands and shouts of ‘it will be chaos!’.

But some decisions are handled locally, like staff-related issues. I’m just wondering which other decisions would be better handled locally, in a way that reflects local conditions. Decisions that help a store to better fit the needs of its local area or the events that happen that day or that hour.

For example, weather can be very local. Different local weather conditions can lead to very different local needs for different stores in the same retail chain. Events happen differently in different towns and urban areas – not all fairs, celebrations, processions and markets happen on the same day. Different local churches, schools, Scouting and other organisations hold events when it suits them. Different places have very diverse histories and customs and we all know the local customers vary – just look at Experian’s Mosaic.

Personalising a store for it’s village

The best way to satisfy a customer’s service-needs is by personalising your service. What I’m talking about here is personalising a store for it’s village, town or urban area.

So how do you let local staff make more decisions to without causing chaos? Answer: keep the decision-making framework centralised – you own it, you control it – and let the staff make their decisions within that structure.

Levels of self-service

If you want to design a decision-making framework where the framework itself is centralised and global but where the decisions themselves are made locally then a good place to start is with the decisions that customers make.

One way to model the customer journey is as a decision-making process. If you can help customers to understand the decisions that they need to make, help them get the information that they need to make those decisions and help them progress along their personal decision-making process then you will make it easier for them to buy from you or buy from you again. This is the objective of lots of content marketing .

Automated services are increasingly being used to ease customers along their personal decision-making processes by helping them make the decisions that they need to make, e.g. Goole Now.

In different industries mobile apps or web sites provide automated analytical services to customers in the form of self-service functions help them as they decide to buy and then as they use firms’ core products and services.

For example, recent regulatory changes to how financial products are sold in the UK have meant that the cost of financial advice is now not included in the cost of the product. This summer we are running a research project to investigate the advice and decision-support requirements that lower income customers have when considering certain financial products.

We hope to understand how to provide low cost, standardised or automated support to fill the advice gap that is felt by customers that are too poor to easily shoulder the burden of bespoke financial advice but with personal financial requirements that are too complex for basic products.

Higher levels of self-service

But self-service is increasingly being used within and between organisations. It is now common for people to be able to update or check their own HR and financial details just by logging in to the corporate intranet. Jobs that would have been done in the past by HR or finance staff.

Self-service is also becoming a way of putting decisions in the hands of the person who is best able to make them. The person at the right place, at the right time or just with a personal understanding of the context of the decisions that need to be made.

The integration of multiple organisational databases, and APIs from outside the organisation, has led to much more data being available and now Big Data analytics is increasingly being used to produce self-services.

Big Data analytics can be used to produce self-services for staff as well as customers. These are based on centralised decision-making frameworks that reflect the global needs of the organisation whilst preserving local flexibility.

If you want to design such a decision-making framework, i.e. a strategy for how your organisation makes decisions that fit global as well as local needs, then think about how you would design self-service decision-making tools for customers and the rest of your staff at different levels.

How can you help customers progress along the decision-making journey of their lives? How can you help your staff progress along the decision-making journey of their job roles?

Decision-making journeys and Big Data visualisation

Here’s a clue: it’s part of the on-going conversation about Big Data visualisation. Some analytical outputs cannot be represented in a pie chart. Some need a 3D graph, some can only be conveyed in a video, some can only be usefully explained in an interactive simulation.

If you want to support (or constrain) people in the decisions that they are making then you need to present then with enough information to make that decision. Too much information and you get information overload. Too little and they will make biased or less useful choices.

Practically speaking, there is no ‘optimal amount’ of information but the human brain can only fit so much in at once. So the objective becomes getting the most out of a brain’s capacity and this depends very much on the way that the information is conveyed, presented or visualised.

The reason why visualisation is developing into a major subset of the Big Data bandwagon is that it (a) the decision-maker is usually not an analyst or a data scientist and (b) visualisation itself is a product of many previous decisions about what information to pass on and how to portray it – filtering decisions and combining decisions.

In other words visualisation is also decision-making. Deciding how to visualise analytical outputs is just as much a part of the decision-making process as the ‘final’ decision that whoever looks at the visualisation makes.

So if you are designing a decision-making framework think then you should about it as an organisational process that starts centrally and globally, in a standard and stable manner, and then moves towards the edges of the organisation where it can be finalised in a varied and agile manner in many different instances.

Think about your decision-making framework as a self-service environment that connects all the people in the business process in question, including the customer and the suppliers.

The benefits of global + local decision-making

Here’s a few:

  • localisation – decisions fit the local context better, they fit the local needs of a situation better
  • personalisation – decisions fit the local customers better
  • agility – decisions are made in a more timely fashion with locally empowerment
  • motivation – staff are more empowered to do their job, the job becomes more satisfying
  • sophistication – a greater amount of all the above points helps you reach further down the Long Tail of your market’s more difficult to reach customers
  • orchestration – making decisions as a network of people working in the same business process can organise and focuse more brains and more senses together

New research project – get involved

Not many firms are thinking about how Big Data will affect their organisational decision-making. So it would be interesting to talk to senior people in HR, business transformation about this or a more Big Data analytics oriented department. We are starting a new research project to look at the organisational design implications of decision-making that is supported by Big Data analytics. Possible research questions include:

1 Which types of staff roles will start to use Big Data analytics technologies, e.g. simulation sandboxes to do low cost, low risk, quick checks of hunches?

2 Which types of decisions will move between roles, up/down, across functions, e.g. nearer to the person who understands the context of the decision or nearer to the enactment of the decision outcome?

3 Which types of decisions will become more data-driven, either real-time, not real-time or on what timescale?

If you want to talk about this subject then please contact me at duncan.shaw@nottingham.ac.uk.

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