Contributed by Daylon Soh 

Not every technology startup is data literate and not every multinational corporation is data illiterate. I’ve worked with organisations on both sides of the scale and hypothesize that an organisation’s lack of savviness in collecting and interpreting data can be an Achilles heel that will handicap growth.

If an organisation makes hundreds to thousands of decisions a day and are taking shots in the dark without the aid of interpreted data (information), the quality of their decisions will be prone to errors and human biases. If an organisation can’t learn the impact of those decisions through collected data (information), poor decisions can’t course correct fast enough and good decisions can’t be doubled down upon fast enough.

As a digital innovation practitioner, I’ve utilised qualitative and quantitative data in marketing strategy and new product development. I’ll skip the benefits of utilising data (information) in an organisation as its importance is well understood.

Instead, I’ll like to propose is a maturity model of data literacy that can help white-collar professionals decide if their organisation is lagging or at the forefront of digital innovation. The effects of how data literacy and digital innovation positively correlate should be self-evident from Fortune’s Top 10 Fastest Growing Companies.

How data literate do you think these companies are? (Observation: more than half of them are internet technology companies working with software.)

Organisation Data Literacy (ODL) Maturity Model

Data literacy

Level 1: Organisation collects raw data without a meaningful categorisation. The organisation has no data analysts. Executive team doesn’t understand or sees little value to invest in data management.

Level 2: Organisation collects raw data with categorisation. The organisation has one or a few data analysts (or a function head plays takes that as a secondary role). The executive team and decision makers rely on data analysts (or function head) to be statistically savvy.

Level 3: Organisation collects cleaned data with meaningful categorisation and correlation consolidated in a dashboard or report. Organisation has an experienced data scientist (or a function head plays takes that as a secondary role) and the executive team is data savvy enough to ask the right questions. Not all collection and interpretation of key business data happens in real-time.

Level 4: Organisation makes data-driven decisions as all decision-makers understand the value of data + statistics and know how to ask the right questions. Some key decisions are still made without the aid of data. Collection and interpretation of key business data happen in real-time with a pool of tools and infrastructure.

Level 5: All decisions made by the organisation are data-driven and all decision makers can obtain and make correlations by obtaining the right data easily. Collection and interpretation of all business data happens in real-time with tools and infrastructure addressing the needs of core business units (e.g. marketing, finance, and product). All decision makers can tell if data presented is biased and can make suggestions on how to improve the presentation and suite of tools.

Which level is your organisation at? What would you and your team need to do to get to the next level?

End-Notes: The Next Frontier

Not all technology startups are built equally. I’ve worked with startups that are in Level 2 and 3 and see how their growth is handicapped by a velocity tied closely with data (information) management. The very best technology startup teams I know operate at Level 4 and 5 at the get-go and enjoy a competitive advantage and adaptive resilience as an organisation. These teams don’t plan in annual cycles, they plan in much quicker iterations.

Last month, the NYTimes reported how Tech Giants are paying huge salaries for Artificial Intelligence (A.I.) talent. “Demand outweighs supply” the article concludes.

If you understand the nature of all computation is a series of inputs and outputs and understand the potential of A.I. A future where machines collect, analyse and build upon heaps of data in real-time is not a matter of IF but a matter of WHEN. It’s a future where competition plays by a fraction of a second very much like the global financial markets taking advantage of information asymmetry through the speed of data transmission and better software today. That future is restrained largely by the limits of computational power which are still doubling every 2 years according to Moore’s Law.

Progress is non-linear from here.

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About Daylon Soh

Bio ImageDaylon works in the intersection between digital marketing, digital product design and digital product management helping startups and corporations build new digital products and ventures. He uses a mix of research methods, Agile practices and communication strategies to facilitate the innovation process with teams. He currently works for Unilever as a Digital Marketing Strategist and was most recently part of the Digital Product Design team of Aviva Digital Garage working as a SCRUM Product Owner. Daylon is also an Agile Certified Practitioner (PMI-ACP)® & PRINCE2® Certified Practitioner in Project Management and instructs adult learners at General Assembly.


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