Data science and decision science are two-sides of the same coin. Chris Dowsett, Director of Data and Insights at Spotify, explains that data science uses data “for improving and developing new products based on robust statistical methods” whereas decision science uses data as a “tool to make decisions.”[1] Although the distinction between data science and decision science is fuzzy, Dowsett insists there is a significant difference between data scientists and decision scientists. “For decision scientists,” he writes, “the business problem comes first. Analysis follows and is dependent on the question or business decision that needs to be made.” For most organizations, the business problem should come first. That’s why, at Enterra Solutions®, we are advancing Autonomous Decision Science™ (ADS®). We believe ADS is the next step in the journey beyond data science.
2023 Decision Science Trends
Trend 1. More Data. The amount of data being generated is not going to abate. Rui Pereira, Data and Analytics Delivery Manager at NTT DATA, explains, “In five years, we will be using more data to make smarter decisions about our lives. In five years, there will be sensors everywhere and as a result, we will have access to more information than ever before. This has the potential to be both good and bad. On one hand, it could bring people closer together by enabling more connection with each other. On the other hand, it could lead to an overload of stimuli which would result in people withdrawing from society.”[2] Decision overload is one reason we believe Autonomous Decision Science will become increasingly important.
Trend 2. Democratization of Data. Decisions are made throughout an organization. In fact, Bain analysts Michael C. Mankins and Lori Sherer assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[3] Therefore, it follows that the data used to make those decisions should be widely available. Christophe Atten, an Assistant Team Manager in the Spuerkeess Data Analytics/Science Lab, observes, “[Data democratization] might be one of the most important, if not the most important, concept to adopt within 2023.”[4] He notes that Collibra defines data democratization this way: “Data democratization is when an organization makes data accessible to all employees and stakeholders and educates them on how to work with data, regardless of their technical background. Plainly put, the ‘data’ in data democratization is any information you could potentially gather about your business or organization.”
Trend 3. Augmented Analytics. Some experts believe AI should stand for augmented intelligence. Augmented analytics supports that notion. Journalist Önder Erdine insists one of “the top data science trends is augmented analytics.”[5] He explains, “Augmented analytics is a vital data science concept that is becoming more popular by the day. It transforms how data analytics is handled, manufactured, and generated by utilizing machine learning [ML] algorithms and artificial intelligence. Augmented analytics tools are now popular because they provide automated chores and insight solutions by using complicated algorithms to enable conversational analytics.” The CNDRO staff agrees. They write, “Augmented analytics is the next big thing in data and analytics. This new technology helps business professionals discover insights from data. Augmented analytics uses algorithms to provide context-aware insight suggestions. It also helps automate tasks related to data preparation. This trend is expected to grow in the coming years. Augmented analytics enables a business user to design the right query for a data set.”[6]
Trend 4. Ethical and Explainable Intelligence. Zohra Ladha, Senior Director of Data Science at Tredence, observes, “As AI/ML becomes omnipresent in every facet of life, from healthcare to governance, the need to white box them also becomes more crucial. Likewise, it will become increasingly important to explain ML outputs and what specific data was used for what purposes. Ethics and fairness in AI/ML will assist in explaining or removing inherent biases to prevent inequitable decisions, making this trend important for 2023 and many years to come.”[7] At Enterra®, we use the Massive Dynamics™ Representational Learning Machine™ (RLM) to determine what type of analysis is best-suited for the data involved in a high-dimensional environment. The RLM accomplishes this in a “glass box” (or “white box”) rather than a “black box” fashion (i.e., it makes decisions explainable).
Trend 5. Data Governance and Regulation. Amit Patel, Co-founder of Bacancy Technology, writes, “Data science is all about data. However, data privacy laws like GDPR and CCPA are putting additional burdens on businesses to protect data. Every company adopting a data science path has to be more alert and cautious during data collection, preparation, storage and usage. It has become mandatory for global organizations to utilize technologies that empower data science but protect data privacy at the same time.”[8] Futurist Bernard Marr adds, “Data governance will be big news in 2023 as more governments introduce laws designed to regulate the use of personal and other types of data. In the wake of the likes of European GDPR, Canadian PIPEDA, and Chinese PIPL, other countries are likely to follow suit and introduce legislation protecting the data of their citizens. In fact, analysts at Gartner have predicted that by 2023, 65% of the world’s population will be covered by regulations similar to GDPR. This means that governance will be an important task for businesses over the next 12 months, wherever they are located in the world, as they move to ensure that their internal data processing and handling procedures are adequately documented and understood.”[9]
Concluding Thoughts
Atten concludes, “If you need to differentiate today who is the winner in a competition, the differentiator will be data, as data is the new gold of the modern era.” As the size and complexity of databases grows, the CNDRO staff believes how that data is stored and accessed will become a higher priority. They write, “Increasingly complex and diverse data has made it difficult for organizations to manage and integrate data. In addition, new data sources and applications have created complexities. Organizations need to manage data in a way that allows them to maximize its value. A data fabric helps unify data sources and creates a single interface for different applications. It helps improve data visibility and access. This solution provides a secure and automated way to move data in real-time.” Journalist Alex Woodie explains, “Conceptually, a big data fabric is essentially a metadata-driven way of connecting a disparate collection of data tools that address key pain points in big data projects in a cohesive and self-service manner. Specifically, data fabric solutions deliver capabilities in the areas of data access, discovery, transformation, integration, security, governance, lineage, and orchestration.”[10]
I suspect you will be hearing a lot more about data fabric and other data storage architectures in the coming year. In the near future, I will provide a brief primer on the subject. In the Digital Age, business focus must remain on data and the insights it can provide. As a result, data science and decision science will remain forever intertwined. Nevertheless, because decision science stresses business problems, decision science will become more important to the business world in the years ahead.
Footnotes
[1] Chris Dowsett, “Data Science vs Decision Science,” Towards Data Science, 24 January 2019.
[2] Rui Pereira, “The Future of Data Science: Where Will We Be in 5 Years?” DataDrivenInvestor, 24 November 2022.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Christophe Atten, “The Top 5 Data Science and Analytic Trends in 2023,” DataDrivenInvestor, 17 November 2022.
[5] Önder Erdine, “Top 5 Data Science Trends for 2023,” Dataconomy, 19 October 2022.
[6] CNDRO staff, “8 Key Data Science and Analytics Trends You Should Look Out For in 2023,” Medium, 24 November 2022.
[7] Zohra Ladha, “Top Data Science Trends to Watch out for in 2023,” Express Computer, 21 November 2022.
[8] Amit Patel, “Top Five Data Science Trends That Made An Impact In 2022,” Forbes, 7 November 2022.
[9] Bernard Marr, “The Top 5 Data Science And Analytics Trends In 2023,” Bernard Marr & Co., 7 November 2022.
[10] Alex Woodie, “Data Mesh Vs. Data Fabric: Understanding the Differences,” Datanami, 25 October 2021.
