It’s that time of year again when I check out the Crystal Skull … er, ball, and make some forecasts of the continuing difficulties and new patterns I predict 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 individuals are confusing Digitalizationover and over again– which is the integration of digital innovations such as cloud native apps and mobile phones into existing functional procedures– with Digital Transformation– which has to do with leveraging the economics of huge information, IOT and advanced analytics (machine knowing, deep learning, expert system) to discover new sources of customer, operational and market price.
Digitalizationreplaces human-centric procedures with sensors to collect use or performance information, whileDigital Transformationuses digital innovations such as artificial intelligence, deep learning and blockchain to develop brand-new sources of consumer and market value, and re-engineer the organization’s business models (see Figure 1).
Figure 1: Digitalization versus Digital Transformation
See the blog site “It’s Not Digital Transformation; It’s Digital ‘Business’ Transforma …” for more information on what Digital Transformation actually requires.
Data Monetization Continues to Be the CIO’s #1 Challenge
I picked “Other People’s Money” as the film that represents the difficulty that the Chief Data Officer (CDO) faces in attempting to drive information monetization. Part of the information monetization problem resides in the reality that lots of organizations perceive the term “money making” as representing a “value in exchange” (what someone is ready to pay me for my information) versus “worth in use” (leveraging the insights buried in the data to create brand-new sources of worth).
It’s an economics discussion, not an accounting discussion!
I anticipate that 2019 is the year when companies’ Chief Data Officers laser-focus their charter around data monetization. As I have stated in the past, I believe leading organizations will rename the CDO title to “Chief Data Monetization Officer” to clarify the charter and distinguish the CDO/CDMO function from that of the CIO, who is focused on handling the facilities that supports the company’s data (see Figure 2).
Figure 2: Data Monetization Starts with the Business
See the blog site “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 method.
Data Lakes Continue to Under-perform
I picked “Automobiles, trains and airplanes” as representative of the battles that lots of companies are having with their data lakes. Data lakes continue to under-deliver, however in 2019 companies will realize that their information lake performance issues are not an innovation issue, however is rather a focus problem. Too lots of companies are too concentrated on using the information lake as a way to decrease the costs related to information (via data storage facility ETL off-loading, information archiving and data staging). CIO’s are missing the larger opportunity to transform their data lake into a collaborative worth development platform around which the organisation stakeholders and the information science team can collaborate to take advantage of data and analytics to power the company’s crucial business efforts such as lowering consumer attrition, unplanned operational downtime, and outdated and extreme stock; or enhancing on-time shipments (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 Attenti …?” for more details on the transformation of the information lake into a company’s collective worth creation platform.
Data Engineering Gets Hot
I selected “Are We Done Yet?” to represent that 2019 is the year that Data Engineering gets its due aspects as a full-fledged member of the information science community. A data scientist is just as reliable as the data that they have with which to work, and for the data researcher to be reliable, they require to have an information engineer partner-in-crime.
The data engineer:
- Co-develops the big data architecture that helps analyze and process data that the organization requires and further optimizes those systems to perform smoothly.
- Collects the data from a variety of traditional and non-traditional sources, stores it in a data lake, cleanses and integrates the data (data prep) for analysis.
- Evaluates, compares and improves the different approaches including design patterns innovation, data lifecycle design, data ontology alignment, annotated datasets, and elastic search approaches.
- “Wrangles” the data which transforms, maps and “munges” the raw data using algorithms (e.g. sorting, parsing) into predefined data structures, and deposits the results into a data lake for the data scientist (see Figure 4).
Figure 4: Data Science Community Roles and Responsibilities
See the blog site “A Winning Game Plan for Building Your Data Science Team” for more information on the functions and duties of your data science neighborhood members.
Over-hyping of AI Delays Business Benefits
I picked “Tin Men” to represent the over-hyping of Artificial Intelligence (AI) abilities in 2019, which I anticipate will get even worse (I expect AI-infused Skippy peanut butter and Cap’n Crunch cereal are just around the corner!). The AI over-hyping around more cultural diversions and human task loss prognostications leads to less development in driving the commercialization and monetization of AI.
But folks are gradually beginning to recognize that AI, specifically in the form of device knowing (i.e., direct regression, logistic regression, choice trees, K-Nearest Neighbor, Support Vector Machine), has actually been around for decades without causing any significant cultural shifts or demise of the human race. I have yet to see a K-means cluster (an unsupervised machine learning algorithm) round up powerless humans for death camps ….
Unfortunately, all the AI over-hyping and consternation will delay the business, operational and society advantages that AI (see Figure 5).
Figure 5: Economic Costs of Over-hyping New Technologies like AI
See the blog site “Why Accept the Hype? Time to Transform How We Approach Emerging Tec …” for more details on the financial costs of over-hyping new innovations such as AI and Blockchain.
The Battle of Tomorrow Is at the Edge
I picked “Edge of Tomorrow” to represent the Battle for the IoT Edge( what, you do not believe that movie was a funny?). And the fight for the IoT edge will be combated by Industrial (OT), not innovation (IT), business. The edge does not start at the IoT gateway, the edge begins at the PLC and the sensors that are generating the information. These PLC’s are getting smarter as more storage, calculate, device knowing and AI abilities are pressed to the edge.
Too numerous IT companies believe of the Internet of Things (IoT) as just another information source to be housed in their storage devices. But IoT is more than simply another data source. IoT represents ability to act at point of information capture; to apply Machine Learning at data capture to optimize functional choices.
That’s the power of the edge (see Figure 6).
Figure 6: Advanced IoT Architecture Supports Edge Data Capture, Analytics and Actions
See the blog site “Tomorrow’s Digital Transformation Battles Will Be Fought at the Edge” for more information about the information and analytic capabilities provided by the Internet of Things.
Finally, the “Game of Thrones” last season will annihilate data and analytics geeks’ productivity in 2019 (See last year’s Game of Thrones blog site Stopping the White Walkers of Data Monetization).
Winter is coming, infant!