Data – the prerequisite for intelligence
Many companies now are striving to be more agile and more innovative – what allows them to do this is data and how they use it. Data is now a prerequisite for intelligence and being a data driven organisation is now an imperative
By 2022, some 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency – 89% of companies see a significant opportunity to drive business value by improving existing products and services through data and analytics.
According to Gartner, 2022 is set to be the year when organisations are valued on their information portfolios. For many of the most successful companies, this is already largely the case. Companies such as Facebook and Netflix place most value not on their technology, but on the data they can harvest and the services they can build on the back of it.
Most organisations don’t get value from their data
Although data and its use is becoming increasingly important, most organisations are still failing to use it to achieve value. Fully half of them lack the AI and data literacy skills to achieve business value, while a huge 87% of data science projects fail to reach production and generate value.
Consequently, these data challenged organisations will experience a raft of issues, such as slower revenue growth, higher operational costs, a delayed time to market, lower customer satisfaction, a disengaged workforce and a lower valuation.
People not technology are the problem
The greatest obstacles to gaining benefits from data and intelligence come from people and staffing issues rather than challenges with technology. Although technology does present challenges of its own, with user friendly platforms now widely available these are not as much of a problem as previously. Technology is no longer the thing that stops us getting benefit from data. Instead, the challenges are due to skills and culture, low data literacy, high expectations, a low trust in data and strategy misalignment.Skills and culture gap
One of the biggest constraints is the talent gap. In many companies, staff are simply not encouraged to contribute to data science projects and also do not understand the issues involved. This leads to a shortsighted approach, with the creation of data silos resulting in fragmented and disjointed insights.
Low data literacy
With a limited understanding of data and its uses and limitations, staff are not able to ask the relevant questions that would increase their understanding and ability to use data. Data reporting teams are simply responding to requests for information, rather than getting to the nub of what the person requesting the information actually needs. Ultimately, this means that insider knowledge is not contributing to data science projects and companies are missing the top use cases that would bring them real benefits.
High expectations
In their role as consumers, people are used to getting access to lots of data and information instantly through a wide range of sophisticated platforms and data visualisations. This carries over to their expectations as employees, and companies are struggling to keep up with these expectations. This leads to projects not being moved to production and an increased risk for data compliance and privacy.
Low trust in data
Data is proliferating across many organisations, but there is a distinct lack of trust in it. Data is not well articulated, and there are few references to help people understand what they are seeing – if people do not trust the data they are given, they will create their own. This can lead to misalignment and conflict between functional areas as teams create their own trusted data rather than utilize data created by other teams.
Strategy misalignment
Many organisations have been convinced of the need to write a data strategy, and many have done so. The problem is that many of these strategies do not effectively link into the business and do not articulate how data will be used to support the wider business strategy. This means that the data strategy becomes yet another silo and does not focus on how to use data to empower the rest of the organisation.
There is no quick fix for these challenges, which need to be addressed as part of a journey towards better use of Data and Intelligence. At NTT DATA, we specialise in helping you make this journey.
Just a 10% increase in data accessibility will result in an additional $65 million net income for a fortune 500 company.
The data and intelligence journey
The biggest challenge in every organisation is that of data trust. This trust is established through Knowledge,Reliability and Engagement.
Knowledge is understanding the business definitions and the context of the data you are using.
Reliability is achieved by having data standards which are clearly defined, consistently applied and their adherence openly published so that all users can utilise the data in an informed way.
Engagement is about having the right stakeholders involved in the governance processes and communications about what data is available in the organisation, how its reliability is being actively managed and enhanced, and how to use it to drive decision making.
NTT DATA will work with you to ensure that your organization maximises knowledge, reliability and engagement, setting you up to make much better use of the data and intelligence you possess.
Ensuring that data becomes a strategic business asset
To ensure that data is useful as a strategic business asset for an organisation, NTT DATA focuses on six broad areas that follow the lifecycle of data. Each of these stages is achieved in practical terms with a number of solutions offered by NTT DATA.
Define data strategies
This stage defines the role data plays in enabling the organisation’s wider business strategy. This is designed to produce an aligned data strategy, a baseline data maturity, a data capability map, a roadmap to achieve the vision and buy-in from senior leaders and middle managers.
Transform data architectures
This stage architects data platforms and engines to support the current and future enterprise, while transforming legacy systems. The result of this stage should e a clear linkage between the enterpriseand data architectures, robust data architectures and a set of refined reference data.
Develop intelligent platforms
In this stage, NTT DATA will rapidly develop best-in-class intelligent platforms that drive business outcomes, which include reduced time to market, increased customer satisfaction and reduced waste.
Deliver data integrity
In this stage, we ensure robust data governance, to ensure a continued drive for data quality, which in turn will allow better business decisions. The expected outcomes of this stage are increased data reliability, reduced data management costs and clear data ownership.
Leverage data assets
Here, NTT DATA will help deliver rapid outcomes through the application of best-in-class data and analytics solutions. The outcomes can include increased data literacy, rapid validation of data science projects, the embedding of a data culture in the organisation and an accelerated business value.
Apply intelligent solutions
In this stage, we proactively solve business problems using better data insights. We expect the client organisation will achieve rapid results, improve ROI by up to 400% and reduce risk.
Delivering data success
NTT DATA has already delivered success for major companies through successful exploitation of data. These include some of the biggest names in telecommunications, retail and financial services.