Top 5 Trends in Data Analytics That Every Leader Should Know

Data in 2023: An Increasing Role of Artificial Intelligence and Machine Learning

There’s no question that the field of data analytics is a constantly evolving one, with new tools and technologies emerging at a staggering pace. A number of these emerging trends have the potential to change the way we interact with and analyze data forever, making this an exciting time for leaders in any industry wanting to get more out of their data assets. With the promise of a groundbreaking year ahead, here are the Keiter Technologies team’s picks for the Top 5 Data Trends to look out for in 2023:

5. Data marketplaces:

   Data marketplaces are online platforms that allow organizations to buy, sell, and trade data, even from competitors within their own industry. These platforms provide a secure way for companies to access a wide range of data sources, especially data that might not be internally available. At the same time, a secure marketplace allows organizations to begin generating revenue from one of their most valuable assets: their own data.¹

Why it’s a big deal: As data privacy regulations continue to evolve and become more restrictive, it is becoming a challenge for organizations to generate all the data needed to gain insights into their customers on their own. A well-functioning, secure, and privacy-focused marketplace will help bridge the gap, especially in conjunction with the next trend on our list.

4. Synthetic data:

   Synthetic data is artificially generated data used for training machine learning algorithms. This data is generated by models that mimic the patterns and relationships found in real-world datasets. Synthetic data has the advantage of being more readily available and easier to control than its real-world counterpart, which makes it useful for testing and developing machine learning models, creating simulations, and driving experimentation.²

Why it’s a big deal: Combining the large volume of data made available through data marketplaces with the anonymity afforded by synthetic data, we are getting closer to striking the right balance between the privacy demands of consumers and the insatiable data needs of modern businesses. In addition to this, data scientists can use synthetic data to avoid developing models that simply reinforce the natural biases of a company’s internal data. For example, if a business has traditionally targeted consumers who are homeowners, any model developed from that data will reflect this institutionalized bias. Synthetic data allows a data scientist to break through these biases and discover new opportunities with new consumer segments.

3. Data mesh:

   Data mesh is an innovative approach to data management that emphasizes the importance of building a shared understanding of data within an organization. Data mesh moves the focus of data control from a centralized place, such as a traditional data warehouse, and places the burden of data management on to the domain itself. For example, analysts and engineers working within the finance department would take responsibility for the data generated through the various financial applications, managed, and controlled separately from other domains such as sales or HR. The key to success with this approach is having a strong set of guidelines, naming conventions, and other meta-data guardrails across the organization so that analysts can easily access and work with data regardless of its domain source.

Why it’s a big deal: Data mesh isn’t a good fit for every organization, especially those early in their evolution with analytics. But for more data mature organizations, data mesh can help to overcome many of the barriers to accessing and combining data that often exists in traditional, highly centralized data environments such as a warehouse or data lake.³

2. Augmented analytics:

   Augmented analytics will become increasingly related to the top trend on this list, but for now it deserves to be highlighted on its own. Augmented analytics involves the use of artificial intelligence and machine learning to automate and enhance many of the tasks involved in data analytics. In addition to automating the time-consuming data cleansing and exploration tasks, the value of augmented analytics comes from its use of natural language processing to allow users to ask questions of their data in a conversational way. In addition, augmented analytics promises a future where machine learning algorithms can easily illuminate insights and trends that are often overlooked by analysts.

Why it’s a big deal: The most difficult challenge for analysts is also the one that can deliver the most value: uncovering hidden insights. By allowing AI to synthesize and rapidly explore an organization’s data ecosystem while automating the steps of data preparation, analysts can spend more time focused on high-value insight gathering, model development, and communication.

1. Generative AI:

   Generative Artificial Intelligence (AI) involves the use of machine learning algorithms to create new content such as images, videos, texts, and music. The most prominent name in this emerging space is OpenAI and their ChatGPT 3.5 next-generation AI chat bot, which has garnered endless astonishment since its release in November 2022. ChatGPT, available free for anyone to try, has opened the eyes of millions of people to the possibilities (and perils) of our rapidly approaching AI future.

Why it’s a big deal: Obviously AI will continue to change daily life in ways that we cannot yet fully grasp, and we will certainly cover this topic more in coming articles. This technology has the potential to revolutionize every industry by enabling the creation of new and unique content on demand, assisting with everything from generating new marketing content to writing legal documents. In analytics, the future will include generative AI being woven into the daily workflow (i.e. augmented analytics), but also improving how new insights are communicated to leaders. Dashboards in the future will not only present graphs and charts but will quite literally have two-way “conversations” with their end user, allowing the user to ask questions of their data in a naturally iterative way. There have been early entries in this space, with IBM, Tableau, PowerBI, and other tools integrating some form of natural language search. And while these features have limited capabilities today, the next generation of AI will become an integral part of every analyst’s toolkit, dramatically changing the way we interact with and present data.

These five trends demonstrate the continued evolution of data analytics and the increasing role of artificial intelligence and machine learning in this field (and beyond). Keeping up with these developments will be critical for organizations looking to make the most of their data this year, so stay tuned with us in 2023 as we explore these trends and much more in the world of data science, analytics, and business intelligence.

If your business is interested in leveraging data analytics to improve processes or assist in growth goals, contact us and let’s talk about it today! Email | Call: 804.747.0000

Sources

¹ “Data marketplaces: The next big thing in data analytics?” by Rachel King, ZDNet, 2020.

² “Synthetic Data: The Future of Data Science?” by Raul Castañeda, Towards Data Science, 2021

³ “Data Mesh: A new paradigm for data management” by Martin Fowler and James Lewis, Medium, 2020

⁴ “The Future of Data Analytics: 5 Key Trends to Watch” by Bernard Marr, Forbes, 2020