2019 Big Data and Data Science Predictions
It’s that time of year again when I look into the Crystal Skull…er, ball, and make some predictions of the continuing challenges and new trends I foresee in Big Data and Data Science for the coming year.
It’s Data “Business Model” Transformation, not Digitalization
Digital Transformation moves beyond just “digitalization”. I chose “Groundhog Day” as representative of how people are confusing Digitalization over and over again – which is the integration of digital technologies such as cloud native apps and mobile devices into existing operational processes – with Digital Transformation– which is about leveraging the economics of big data, IOT and advanced analytics (machine learning, deep learning, artificial intelligence) to uncover new sources of customer, operational and market value.
Digitalization replaces human-centric processes with sensors to gather usage or performance data, while Digital Transformation uses digital technologies such as machine learning, deep learning and blockchain to create new sources of customer and market value, and re-engineer the organization’s business models (see Figure 1).
Figure 1: Digitalization versus Digital Transformation
See the blog “It’s Not Digital Transformation; It’s Digital ‘Business’ Transformation” for more details on what Digital Transformation really entails.
Data Monetization Continues to Be the CIO’s #1 Challenge
I chose “Other People’s Money” as the movie that represents the challenge that the Chief Data Officer (CDO) faces in trying to drive data monetization. Part of the data monetization problem resides in the fact that many organizations perceive the term “monetization” as representing a “value in exchange” (what someone is willing to pay me for my data) versus “value in use” (leveraging the insights buried in the data to create new sources of value).
It’s an economics conversation, not an accounting conversation!
I predict that 2019 is the year when organizations’ Chief Data Officers laser-focus their charter around data monetization. However as I have stated in the past, I think leading organizations will rename the CDO title to “Chief Data Monetization Officer” to clarify the charter and differentiate the CDO/CDMO role from that of the CIO, who is focused on managing the infrastructure that supports the organization’s data (see Figure 2).
Figure 2: Data Monetization Starts with the Business
See the blog “Data Monetization? Cue the Chief Data Monetization Officer” for more details on the expanded role of the Chief Data Officer’s responsibility in driving an organization’s data monetization strategy…
Data Lakes Continue to Under-perform
I chose “Planes, Trains and Automobiles” as representative of the struggles that many organizations are having with their data lakes. Data lakes continue to under-deliver, but in 2019 organizations will realize that their data lake performance problems are not a technology issue, but is instead a focus issue. Too many organizations are too focused on using the data lake as a way to reduce the costs associated with data (via data warehouse ETL off-loading, data archiving and data staging). CIO’s are missing the bigger opportunity to convert their data lake into a collaborative value creation platform around which the business stakeholders and the data science team can collaborate to leverage data and analytics to power the organization’s key business initiatives such as reducing customer attrition, unplanned operational downtime, and obsolete and excessive inventory; or improving on-time deliveries (see Figure 3).
Figure 3: Data Lake is a Collaborative Value Creation Platform
See the blog “Realizing the Potential of Data Monetization…Do I Have Your Attention Now?” for more details on the transformation of the data lake into an organization’s collaborative value creation platform…
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By Bill Schmarzo