How to build a data-driven strategy: theoretical frameworks and evidence-based results
By Alessandro Piva, Research Officer Big Data Analytics
From Traditional to Big Data Enterprise: an overarching framework
Become data-driven does not mean carrying out a single project in a single functional area. It does not mean to buy a new technology tool or to hire a junior Data Scientist.
Investments in innovative technologies – and in Big Data Analytics in particular – are extremely cross-impacts and they involve both hard and soft skills, from computing architecture to change management. Then, in order to extract value from Big Data, it should be defined an all-encompassing strategy.
The Big Data Analytics & Business Intelligence Observatory has developed a framework called Big Data Journey. It is an instrument that sums up knowledge deriving from literature analysis, companies’ interviews, case studies and quantitative surveys.
The instrument aims at a dual objective. From one hand, every company can assess its own maturity and use it to evaluate its adoption path; from the other hand, the Observatory can use it to provide a full picture of where innovative, average and late companies locate themselves.
Big Data Journey: a little taste
The model identifies four main variables, each of them characterized by five maturity levels:
- Strategy: it refers to organization approach to Analytics in the middle or long run. In a traditional enterprise, no Big Data Strategy exists. As awareness increases, a long-time plan is established, Top Management are main sponsors and the achievement company mission is strictly linked to Big Data Analytics projects;
- Data: it refers to how data are managed, stored and deployed to specific applications. In a traditional enterprise, data are usually fragmented, they suffer from bad quality or inconsistency. In a Big Data Enterprise, data are complex, heterogeneous (both structured and unstructured) and voluminous, but there is a good Data Governance strategy.
- Competence and governance: it analyses the presence of Data Science competences and governance choices to manage them. At first, a business line could decide to start a Big Data project going through outsourcing, but, as Analytics advantages become clear, the company will set multi-disciplinary teams committed to Data Science projects and Data Literacy in the organization increases;
- Technology: it analyses infrastructural approach to manage Big Data. In a traditional situation, data are fragmented in closed silos and technology does not allow to exploit Analytics potentialities. In the opposite situation, technology allows to manage heterogeneous and coming from different sources data, in real time, if it is needed.
What’s next? Big data technology comes of age
Monitoring the startup ecosystem (firms founded in 2013 or later) represents a good approach to understand how Big Data technologies are evolving and where investors’ interest is going. Therefore, the Observatory conducts every year an international census on startups operating in the Big Data Analytics market.
The last Research available (2018) identified 443 startups. The total investments received by these companies amount to $4.74 billion.
What can we learn from startups? Quick reference!
The Big Data technology supply chain is achieving a higher level of maturity. The majority of startups offer applications, i.e. specific tools already customized to support an industry or a business function. However, if you build a new hard technology tool, such as an innovative database or a streaming computing architecture, probably you will receive a higher funding: disruptive innovation begins from foundations!
The United States are the incumbent, most of startups have born there. But if you look for outstanding outliers, turn your head to the East and get used to ideograms.
The Big Data Analytics & Business Intelligence Observatory
Born in 2008, the Big Data Analytics & Business Intelligence Observatory represents a permanent point of reference for research, knowledge and communication on innovative potential of Big Data Analytics. Our purpose is both to produce and spread knowledge about opportunities and impact of Big Data Analytics in companies and public authorities. Our approach consists of interpretive models based upon sound empirical evidence (around 1000 companies involved every year) together with centres for independent ongoing and pre-competitive discussion to bring together the demand-and offer-side.
The Big Data Analytics & Business Intelligence Observatory will be part of Big Data Paris 2019. Come visit us at zone D – between stands D26 and D27 – and take part of our speech at Workshop E, 12th March, 12h-12h30.