7 successful strategies for becoming data-driven

Rob Rich explains why without strong leadership, effective data management, organizational transformation and continuous improvement, technology can be as much a source of frustration as success. Transforming to become data-centric needs a carefully planned, multi-faceted, disciplined approach – here are some of the most important things you need to do.

Organizations in all industries are increasingly recognizing the value of data-driven business strategy and operations. Advances in computing, data management and analytics are enabling new initiatives in key areas of business, promising to transform companies in a way not seen for two decades or more, when business process re-engineering led the way to increased productivity and profitability. Indeed, data is seen as the new oil, and a driver of business innovation and competitive differentiation.

Moving to data-centricity is not just about technological advances – corporate leadership, talent and the right processes are equally important. Without each of these key elements, organizations will struggle to gain and maintain leadership.

TM Forum’s primary research into data analytics over the last five years has identified a number of areas that companies must addressed if they are to transform to become more data driven and realize the benefits. Although the road to success has not necessarily been smooth, many fundamental principles around data management remain relevant.

1. It’s all about the business value

Organizations should start by asking themselves, what are my greatest business opportunities and/or problems? Next they must focus on how a data-driven approach can contribute and provide value: Many organizations are caught up in the technology rather than its application. They ask, “What can this new technology do?” instead of “What problems do I need to solve?”. The situation has been exacerbated by how fast the technology has advanced, creating a skills shortage. Certainly some experimentation is in order, but organizations need to stay focused on the primary goal – driving the business forward. Successfully harnessing analytics will accelerate the benefits of adoption and increase momentum for further deployment.

2. Management must ‘walk the walk’

For the last three years our analytics research has identified management commitment as the second-highest scoring critical success factor (see figure below, taken from our Insights Research report, Feel the heat: Big data lifts off).  While top management sponsorship and approval is essential, management has much more than just a stewardship or enforcement function. The individuals must ‘walk the walk’, embracing fact-based decision making, pushing for more and better data, and recognizing achievement when efforts succeed.

Management must also provide a clear vision, prioritize analytical applications, understand return on investment, allocate appropriate resources, manage talent, ensure cross-functional coordination, and remove some of the barriers that will inevitably pop up during implementation. Finally, management must insist on compliance with legal and regulatory requirements for data in areas such as security and privacy.

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3. Care for your data like it’s your lifeblood; because it is.

Data quality and integration issues were seen by respondents in our 2015 research as the top inhibitors to success, by a big margin. The phrase ‘garbage in, garbage out’, coined in 1963, is still right today. Unfortunately, data is found in every nook and cranny of the service provider organization, in every imaginable format, and often in conflict with the similar data from other sources.

Data governance programs must be aligned with and driven by organizational goals, defining strategies, policies, processes and standards in support of those goals. Organizations should assess their current state and develop plans to achieve an appropriate level of maturity in governance over a period of time. It’s important to recognize that governance is never complete, it will evolve, just as corporate needs and goals, technology, and legal and regulatory aspects do.

Governance maturity models can also help organizations assess their current state and to define their target state. Management needs to recognize that achieving strong data governance is often not a short term goal; for most large organizations it can be a multi-year effort. Aligning governance programs with business priorities is therefore important, as it can help to demonstrate a return on investment.

4. Standardize to scale, fast and more cheaply

Data standardization is also an important component for success. Organization can’t hope to achieve goals like a 360-degree view of customers or end-to-end management of services, networks or logistics without a common set of data definitions and structures. TM Forum’s Information Framework (SID) was developed by and has been evolved over many years by professionals from the communications and information industries working collaboratively to provide a universal information and data model.  This common model’s benefits include faster time to market for new products and services, cheaper data and systems integration, less data management time, and reduced cost and support when implementing multiple technologies.

Moreover, organizations should seek standardization in their analytical data structures, just as they do in their transaction data structures. Traditional analytics and business intelligence used operational data stores, data warehouses, and data marts as their primary data repositories. They are still highly valuable to data-driven organizations, but big data analytics require a different structure to be effective.

To address this need, TM Forum is developing an Analytics Big Data Repository, as part of the Big Data Analytics Solutions Suite. This is documented in the Forum’s Big Data Analytics Guidebook and is being progressed, in iterative stages, through the Forum’s proof of concept Catalyst program.

5. Strive for continuous improvement – it pays.

Savvy management teams understand that attempting to ‘boil the ocean’ when adopting a data-centric strategy is a fool’s errand. Some have already learned this the hard way though less than successful ‘big bang’ transformation projects. Given the scope and complexity of emerging value fabrics and the dynamic nature of the digital world, most are coming to understand that the change will continue to accelerate, even as they progress towards to their target state.

So management must set visionary goals for data centricity, but allow for change during implementation and even perhaps to change the vision itself.  Given these realities, it is almost imperative that management approach both data management and analytics initiatives from a continuous improvement perspective, driving progress toward goals while following a roadmap that is adjusted as business and organizational needs evolve.

6. Talent is a huge component of success.

Managing and merging the many, varied sources of complex data, building analytical models and deriving actionable insight from those models requires considerable skill. Proper development of that skill and talent retention are critical to any company’s success as good analysts and data management staff are hard to come by. Those staff must acquire and master a broad variety of skills, including quantitative and technical, business knowledge and process design, relationship building and consulting, and coaching to help others.

Emerging technologies and capabilities such as machine learning require great skill to evaluate, select and deploy. The ability to digest complex analytical output and develop a compelling business story from it is still a relatively rare talent. Analysts by their very nature are highly motivated by challenging and interesting work, allowing them to hone their skills and gain a sense of personal progress is important to them. Employers must recognize these needs and traits, particularly  in this time of such rapid technological advancement, and create appropriate training and growth opportunities for analysts, as well as reward and recognition programs, if they want to keep them.

7. The need to compete 

Recently, we have heard some suggest that ‘analytics are getting easier’, and in some respects this is true. There are off-the-shelf analytics, and some of the new self-service analytical applications have removed some of the complexity in some applications. However, these tools are available to everyone, so while they offer opportunity for improvement, they are not necessarily sources of competitive differentiation. Moreover, while analytics may be getting easier, data management is becoming harder, due to the growing variety of sources and structure, and the ever-increasing velocity at which it is generated. Hence having the right talents and skills is critical, and will remain so for the foreseeable future.

Summing up

Technology remains important to organizations striving to become more successful at leveraging data and analytics, but without strong leadership, effective data management, organizational transformation, and continuous improvement, technology can be as much as much a source of frustration as a source of success. Evolving to become a data-centric organization promises competitive differentiation, but requires a carefully planned, multi-faceted, disciplined approach.

- See more at: http://inform.tmforum.org/features-and-analysis/featured/2016/03/7-successful-strategies-for-becoming-data-driven/#sthash.ylq88KQ7.dpuf