Big Data – the tech gets easier, yet leading change gets harder
And what is really important is USING the insight from the data!
There is much talk about Big Data, yet insufficient advice for organizations to take strategic advantage of it. 90% of all data ever created was created in the past two years alone – and the rate is accelerating.
Today, whilst Big Data technology is ever evolving and increasingly sophisticated, it is actually not the core concern. The real issue is making day-to-day sense of it the insight inside the business, driving action and requiring change. Most enterprises are scratching the surface in effectively taking advantage of Big Data.
It is estimated that only 18% of data that corporations already have is effectively utilised. Yet in our experience, too many firms simply start with the data they have, and then try to take advantage of it.
The real power of Big Data comes from starting not with the data, but with the strategic question
“What problem are we trying to solve – or what opportunity do we want to grasp – and how can data driven insight and processes help”.
Then, define all of the data and the technology needed to deliver the result. Increasingly, to get real competitive advantage, this means taking the data the firm has and adding new data sources to it.
The mantra should be “right data – deep analysis – clear insight – measurable action”. Often, the most successful programs then drive multiple actions, in small but additive steps, and in real time.
The major source of failure is that the insights derived from the data are not fully actioned because the leadership is lacking, and organization structures and change processes are not embedded in the enterprise.
The Customer is in charge – it’s a “personal data for services” exchange
On top of this, there is another revolution.
That is, only the individual is fully able to access all of his or her own data – companies must come to terms with this new data exchange and develop strategies accordingly.
Strategies for Big Data
There are two fundamental strategies to make full use of the insight from “Big Data” and turn it into practical action and effective business decisions.
These are Customer Centricity and Innovation Networks.
1. Customer and client interactions are all moving from “push” strategies to “pull”. Instead of businesses “pushing” services and products at customers, the individual can now discriminate and “pull” services to them – to suit their exact needs, preferences and timing.
“Big Data” makes this possible. Individuals can view recommendations from other customers, access products, services, resources and media that they need, and optimize how and when it is all delivered and how it is subsequently used. In other words, customers “pull” and personalise everything rather accept things being “pushed” at them.
For example, in health care, patients become “expert” about their own ailments via web search, specialised sites and social forums, before visiting the doctor, after diagnosis and during treatment.
Customer centricity is about meeting customer needs, and using data-driven insights to build effective customer programs and offers. This means a move in peoples mind set, enterprise strategy and detailed execution. The business must embrace data driven decisions and use a common customer language to connect things up.
The goal should be to treat each customer with a personalized and engaging experience, and using data-driven insights to build seamless customer touch points. The entire enterprise – activities and processes – should be oriented around the customer, and must use a common customer language to connect people at all levels of the enterprise.
2. Innovation Networks are the second strategy that allows business to take maximum advantage of “Big Data” and associated technologies, to speed up the flow of new ideas, products and services.
An objective look at the business’ approach to innovation is essential. Often innovation is focused internally, yet ideas can come from anywhere – suppliers, customers, universities and even government.
Leaders thus need to embrace new technologies, vast data sets and operational processes that dramatically open up innovation via open networks. They must proactively build networks of internal and external resources, with dynamic structures that change the way the organisation innovates.
What is Big Data?
The “classical” definition of Big Data starts with its volume .. courtesy of Gartner and IBM.
But this is a rather “technological” definition. We need to think about business strategy and its impact on people. So we offer this:
“Big Data” is 6 C’s
- complex: comes from multiple sources – structured databases and unstructured social
- combine: “tiny” and unstructured
- compute: capture, process, analyze, visualize
- create: drives decisive action – changes everything in the organization’s processes
- connected: impacts everyone in the enterprise
- customer: provides individual control of the data
To look at this another way:
Tiny Data + Unstructured Data = Big Data
We should stop calling it “Big”, it’s just “Data”. Then, some definitions:
- Structured Data is data from a single source in a structured format, which, whilst it may in a huge quantity, is actually fixed in its complexity. Often huge datasets like this are considered “Big” simply because of volume, variety and the velocity with which they are formed. An example would be Tesco’s use of Clubcard customer loyalty data dating from the 1990’s.
- Unstructured Data is data with no fixed database format or pre-determined structure. Think of messages and posts on social networks, Twitter, images uploaded to the web, Facebook likes, phone calls, customer service calls and so on.
- True “Big Data” combines the two. Technologies are available to combine and make sense of multiple sources – and turn analytics into useful insight, clear visualisations and action plans. A Big Data “truth” is that simple algorithms on big data outperform complex algorithms with less data.
- Open Data is data made freely available by the data owners or generators, to allow third parties to use and combine with their own data. For example, the UK government is a leader in the field, and data such as census, weather, transportation, health and social mobility are freely available.
Consider the example of looking at someone’s Facebook timeline, and noting that they tend to like wearing blue but never orange. If you are a clothing manufacturer, and knew that fact, wouldn’t that help you make more appropriate offers to that potential customer? And if you could match this insight against the customer’s purchase records over time, wouldn’t that give a richer insight into their behaviour?
Leadership in a Big Data environment
In a discussion with colleague and business innovator Chris Meyer, we noted that Big Data and its application would increasingly allow us to automate processes (and even decision systems) in organisations.
Today, the leader is effectively the pilot of an organization, steering it in a certain direction, and making changes as needed. The pilot drives the decision processes.
But in the world of Big Data, what’s the role of the pilot when decision processes are automated?
From changing strategies, decision processes and self-definition, there are massive challenges to the Leadership role in the era of Big Data.
Use a step-wise approach
The start point is an understanding of the enterprise’s business model and strategy, and move onto an assessment of the current data operations – what is collected, how “fit for purpose” it is, how is it analysed and actioned, what decision processes are impacted etc.
Ask the question “What are we trying to do, and what data do we need“.
Don’t start with “How can we use the data we already have“. You will use it, but first ask the strategic questions properly.
Then consider the overall enterprise strategy, with particular emphasis on the customer. The question is whether performance can be improved either by using current data in different ways or by utilising new data sources.
Once an approach is agreed with management, a detailed plan must be drawn up to turn the insights from the data into action, with the appropriate technologies and analytics – and, importantly, embed the use of the insights in the right organizational leadership and decision processes.