While there are few doubts about the potential economic value of Analytics or Big Data and to larger extend digitalisation, many transformational initiatives struggle to deliver their promises when dealing with real projects, real people and real challenges. Indeed if industry leaders are unanimous on the need to "go Big Data", the lack of evidence on safe return on investment as well as the difficulties encountered along the way provoke understandable apprehension and reluctance. Fortunately factors of inertia can be curbed: advanced analytics integration is indeed not only a matter of IT, data and algorithms, this is first and foremost a question of culture.
Wrong understanding of Data-Analytics leads to painful integrations
"Big Data" is usually presented as a revolutionary way towards gain benefits through extracted, stored and analyzed data from internal and external data sources. That's mostly true: on average, leading companies generate 12% of their revenues from Big Data. But "Big Data" is first of all a Big Bag where Big Promises lie next to a lot Big Difficulties. Most of the decision-makers indubitably recognize data-analytics' value for their organization but a lot of companies still resist adopting this new era's rules and necessary operational changes to execute, especially because of a certain lack of evidence on its business impact. Most business leaders think that it is more and more difficult to obtain a competitive advantage with analytics.
A lot of companies usually underestimate the need to anchor analytics capabilities in the organization and are either to cautious or too greedy
Above all, Big Data and data-analytics are a matter of change and the main dares for most companies are not related to technology: the most dangerous reefs to integration lie in cultural challenges: organizational alignment, resistance or lack of understanding, and change management.
Companies also experience huge difficulties finding the right time and scale for digitization. A lot of them have tried and failed because they misunderstood all or part of data-analytics integration: they usually underestimate the need to anchor analytics capabilities in the organization and are either to cautious or too greedy. Before companies can make benefits from data, they must first set up the fundamental building blocks of a successful data-analytics program. And these building blocks are developing a long-term response to digital impact, adapting the organization's structure, assessing available talents and skills and - above all - adopting a new data-centric culture.
Introducing cultural changes step by step
Companies need to build a "data-first mentality" internally at every scale before embedding any change program towards successful digital transformation. They need to be convinced that data - internal, external, available or even hidden data - can help growing and improve decision-making. However organizations need to progressively experience the benefits of change in order to create momentum and change their DNA. This is the only way traditional companies can catch up with digital natives like Google, Amazon, Facebook, Alibaba or Tencent.
A good first step in the process leading to a success integration of data-analytics in the organization is to consider building a "data-culture" and then experiment it internally. No magic digital transformation tool will help improving productivity, cost reduction or simplification without firstly explaining the need to change and convincing about data-analytics philosophy.
A good first step in the process leading to a success integration of data-analytics in the organization is to consider building a "data-culture"
Data can be extracted and grasped everywhere and represent potential insights that have to seen as a gold stock. Data from internal sources are numerous and mostly underexploited, especially when unstructured or subjective. Before looking towards appealing new horizons at the outside, companies have the opportunity to make a decisive first move into data's world with less dangers or unknowns by investigating their own hidden gold stock: decisions and actions can be seen from different points of view and compared and measured across all departments and all projects within the company; each collaborator is able to feel what her data brings to the company's strategy; each process can be assessed in the context of the new implemented "data culture".
A first experimentation: advanced data-driven management
Because only few executives have a clear view of how analytics can create value for their company and especially in their working routine, we propose a first use case which allows both building a "data culture" in the organization and create value. The idea is to extract underexploited data within projects across the company, embarking every collaborator in an easy and quick participation into a first data-driven management experimentation : each week, collaborators from different projects send feedback about their set of mind and opinion about specific project-related subjects. And data-analytics combined these feedback data with multiple other source and produce visualization, analysis and recommendations for decision-makers and business leaders. Such a tool would allow improving decision positive impact ratio and reacting quickly to well-detected risks and issues.
Such a tool would allow improving decision positive impact ratio and reacting quickly to well-detected risks and issues
An organization is certainly made of collaborators, processes and decisions and each of them is a data producer: a "data-first mentally" can introduced by letting people know that their opinions and actions - aggregated, weighted and analyzed - can lead to enhanced strategic next-moves, gains of productivity or tailor-made management. For instance, one can choose to adopt a light and simple tool aiming at value available data while mobilizing teams about the ongoing changes.
When relevant data-generation tools and metrics are implemented, assumed and used by collaborators, more than just getting internal and external benchmarking opportunities, weak signals detection, teams engagement easy monitoring or multiscale next-best-actions leads, a first battle is won: the gap between intimidating hyped keywords and people is reduced and workers henceforth know what could be the hidden value of data. Hence the main first pitfalls are sustainably overcome.
Using an advanced management reporting tool can be a comfortable tradeoff between data-analytics strategic urgency and leaders' legitimate hesitations
Using an advanced management reporting tool can be a comfortable tradeoff between data-analytics strategic urgency and leaders' legitimate hesitations. While providing decision-makers with individually relevant information at the right time and allow them to carefully adjust company’s decision-making processes, a continuous feedback strategy allows establishing a "data culture" and maintaining a performance culture. Through a simple first internal step, by adopting a continuous internal diagnostics policy, any company would be able to properly better invest in future data-driven initiatives, knowing that people are more familiar with data-analytics stakes and participate in the organization's change program. When this data culture is embarked on every internal project, next data transformation steps will be easier.