IT technologies are rapidly changing our lives. Whether it’s your daily grocery purchase, monthly bill payments, booking railway tickets, or receiving online healthcare consultation, data technologies have penetrated every business model, large, medium, or small. Recent cloud platforms, coupled with Big Data and IoT technologies, have ushered in a new era of “smart technologies” powered by Artificial Intelligence (AI) and Machine Learning (ML) to provision an additional layer of automated benefits to the consumer who is not tech savvy.

How Has AI Changed the World of Data Modeling?

Data Modeling is no exception to this AI wave – new packaged algorithms are gradually changing how Data Science projects are pursued and executed, as well as how traditional practices such as MDM, Metadata Management, and Data Governance are completed. No longer do businesses need expensive data centers with high-caliber data scientists to solve daily business problems. As business goals and technology goals continue to converge over the long term, more business users will find themselves in a new era of Data Modeling, which is part automated, part manual – offering more power and control to the citizen data scientists and other business data crunchers.

With recent advancements in AI and ML, Data Modeling has gone through considerable changes. This year, you will notice smarter machines with the potential to build “conditional capabilities,” according to the Chief AI Strategist at PROS, Michael Wu, Ph.D. Moreover, the supply-chain systems across the globe this year may start showing “prescriptive intelligence,” enabling businesses to outpace consumer expectations. 2019 will also experience transformative, AI-driven clinical-support systems in global healthcare.

Another example is found in the retail food industry: McKinsey’s report The Consumer Sector in 2030 explains how 3D printing technology will provide fresh avenues for consumers to connect with food vendors online for a transformative buying experience.

2018 Trends in Data Modeling explains that though “algorithmic intelligence, self-describing data formats and standardized models” took some of the labor out of Data Modeling, the newer database technologies like NoSQL/non-relational databases and data lakes have initiated a whole new set of challenges for data modelers.

Challenges in Current Data Management Practices

There are many challenges in the Data Management and Data Modeling landscapes these days, including:

• The move away from relational data models: Data Modeling Trends in 2018 reported that data “structure modeling” was as important for data modelers as Data Modeling in 2018, as databases are as widely varied as data types and data pipelines themselves. The practice of using automated data models through Machine Learning algorithms became prevalent last year, and this year, the trend is expected to spike to open analytics opportunities for citizen data scientists. Newer technologies like non-relational databases or data lakes have necessitated a move away from RDBMS systems to innovative ways of handling troves of unstructured data.

• The problem with pipeline-driven data integration: Recent data integration technologies have moved away from an “extract-transform-load” approach to a pipeline-driven one, which has likely complicated the Data Management ecosystem with unknown Data Governance issues commonplace in a distributed data landscape. Businesses are currently struggling to keep pace with the rate of data generation from widely distributed and disparate channels. The Year Ahead: Data Will Drive the Enterprise in 2019 includes a topical discussion about the challenges of data integration post- Big Data and IoT eras.

• Lack of collaboration and disintegrated technology networks: Pushing the Construction Technology Ecosystem to New Limits states that this industry is rapidly moving from disintegrated construction technologies that left seamless collaborations between machines, filed workers, and back-office staff impossible. The packaged algorithm era (algorithm economy) will push the construction ecosystem toward investing in ready-made digital solutions that are highly advanced and integrated.

• Lack of Data Governance in new Data Modeling practices: Data Models Create “Living Policy” for Data Management explains why organizations “must invest in creating an AI-worthy data environment” to realize the business benefits of automation. The article states that about 60 percent of enterprise decision-makers are deeply concerned about Data Quality. It quotes Donna Burbank, Managing Director of Global Data Strategy: “Data Models can form a critical link between business rules and supporting data systems.” Burbank believes that enterprise can meet Data Governance objectives through data models and Metadata Management.

Data Modeling Trends to Watch in 2019 

• Business decision-making, using superior AI and ML capabilities, will be driven by advanced AI features related to analysis of human emotions, reactions, and non-verbal responses as available through unstructured data pipelines.

• The rise of Robotic Process Automation (RPA), provisioned by cloud vendors through Machine Learning-as-a-Service (MLaaS) as recorded in AI Trends in 2019: MLaaS, Robotic Process Automation, and Big Data, will compel data modelers to keep a firm eye of governance issues this year.

• 2019 AI Predictions from Forrester: Data Quality a Top Challenge expresses concern over “irrational exuberance for AI adoption.”

• Being exposed to many AI-driven analytics solution providers in the market, business owners will be more cautious about their investments in packaged Data Modeling solutions.

• Two types of data models may be available to business owners and operators this year. Type A caters to seasoned data professionals for personalized analytics experiences, and Type 2 data models may work as “plug and play” solutions for citizen data scientists looking for quick and accurate solutions. Data Modeling and NoSQL: Innovation and Flexibility highlights specific solution providers who are making Type 2 Data Modeling happen through their innovative technologies.

• With the rise of IoT data pipelines, data professionals will come to expect more “structured data points” in their analytics systems this year, thanks to ML features.

• As more edge computing will be used by businesses this year for IoT analytics across network, the marriage of edge and automated Data Modeling may be an interesting case to study for business analytics experts. Seven Hottest Analytics And Big Data Trends For 2019 discusses what’s coming in business analytics. Also, Top 7 Big Data Analytics Trends for 2019 describes how edge computing will erase the need for network bandwidth by enabling Data Analytics locally (on a fog layer), close to the source of data. This has revolutionized Big Data opportunities for businesses.

• More open-source platforms Like GNU and R may be made available for curious data enthusiasts and aspiring business users to explore Data Modeling with real-life business problems.

• Automated algorithms in packaged data models will drive business analytics across global industry sectors. According to The 10 Strategic Technology Trends for 2019, Gartner’s prediction of 40 percent automation in Data Science tasks will happen a year from now. Likewise, automated ML models will drive analytics by 2020, when more citizen data ccientists will fill the ensuing Data Analytics talent gap left by data scientists. 

• As business analytics move from “predictive” to “prescriptive,” ML-powered data models will have to deliver the expected results.

• Predictive models themselves will hopefully be able to keep up with the latest trend of providing custom solutions across industry sectors. The healthcare industry will witness advanced semi-automated Data Models in radiology and oncology applications, which may be used for research purposes.

• In the retail business, AI-driven cloud platforms will utilize superior video-conferencing systems to enable automated joining facilities, analysis of meeting proceedings, and in-depth understanding of participant reactions. According to Nikki Baird, Vice President of Retail Innovation, Aptos, “an increasing availability of Artificial Intelligence (AI) capabilities driven by cloud computing … will make its way into video conferencing in 2019 in everything from meeting room activity analysis and efficiency …”

• According to Rajarshi Gupta of Avast Software, in 2019 more AI solution vendors will provide applications with capabilities for sharing cryptography-powered data with third parties for powerful insights without compromising data privacy.
  

And one last thought: Will quantum computing penetrate enterprise data systems this year? Many IT industry insiders feel that the future of tech belongs to the company who builds the first quantum computer. And if so how will such a new technology change how Data Modeling has to be done?

Certainly, the rise of non-relational databases has altered how data models are created and leveraged in enterprises, but the hybrid mix of database technologies has necessitated the continuation of traditional Data Modeling practices across organizations. Relational systems are not going anywhere and in many places are more important than ever. But, new non-relational databases have changed the way the old guard data modelers have to look at their jobs. With the growth of AI, Machine Learning, IoT, and advancements in more and more automation, these trends will certainly continue to alter how Data Modeling is accomplished.

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