No matter the industry or size of a business, the companies that will win in 2023 are those that focus on data. Yeah, we said it. The problem is, data volumes are exploding and the ways you can use data are growing even faster, so where do you start? TimeXtender can help.
Regardless of where you are in your data management journey, here are the top six trends in data engineering, science, and analytics to watch in 2023:
Come along for the ride, won’t you?
Prominent companies that deal with massive loads of public information have been using real-time data in order to enhance the safety of their clients. For instance, Meta and other social media sites crawl thousands of gigabytes of information every second (including all your cat photos). Analyzing real-time information has enabled social media sites to curb fake news before it spreads and learn which ads to display to different users depending on factors such as location.
Another major advantage of real-time data analysis is that it can be used to promote businesses through the prediction of customer purchase behavior. For instance, you can analyze the clickstream information that shows which visitors viewed your website and determine the kind of promotions and offers to display in front of them. Financial institutions can also use real-time data in 2023 to monitor transactions as they happen across the globe. This will help detect early signs of fraud and eradicate it in its earlier stages.
As data science evolves, data engineers should make a note of real-time data as it gains more popularity in 2023. More companies are already looking for data solutions to give them a competitive edge. This means data engineers and analysts should develop advanced data strategies that will increase the ease of access to precious up-to-date information, such as real-time inventory updates, making life much easier for your purchasing department.
Every data engineer is responsible for ensuring that the different platforms that make up the modern data stack (MDS) work properly. However, the issue is not the working of each individual platform – the main point of integration is ensuring that each platform works harmoniously with the others. Unfortunately, the MDS has shifted from one or two primary tools that run all functions to a collection of several tools. Each tool has a specific role to perform, but all of them should communicate smoothly with each other (which many of them do not).
TimeXtender is an excellent example of a holistic data integration tool that eliminates the need for an unwieldy MDS. Our software solution helps you to define data marts for several Business Intelligence (BI) tools and endpoints. It also helps you model a data warehouse and quickly integrate your siloed data into a single solution. You can achieve all the above through a simple drag-and-drop function thanks to our easy-to-use interface.
Another big trend to watch out for in 2023 is data security and governance – how your data and the data of those you serve is protected. In our opinion, there have never been sufficient strategies and mechanisms to keep personal data from falling into the hands of unscrupulous (aka shady) people. Governments and businesses are now realizing that the information they collect may be at risk and are moving with speed to install relevant security features. They also need to understand how data is processed along every step in their systems (aka data lineage). It is now more important than ever to use the right tools that support these growing needs. For example, TimeXtender automatically generates documentation and data lineage reports to save users time and prevent errors.
Companies have an important task over the next year to make sure that their data handling and data processing procedures are adequately understood and documented. This means auditing the type of information in their possession, how it is collected and stored, and how the data is used.
While this sounds like extra work for businesses and organizations rushing to comply with government regulations, it provides a great opportunity for data specialists in many countries to showcase their skills and improve their data analysis methods and strategies. Additionally, businesses and organizations are likely to pay well, knowing that their data will be in safe hands.
Every organization should use data produced daily to remain competitive. Some of these companies are relatively small, and not all of them will have the access to data analysis and storage facilities like other prominent tech giants. This is why most of them are turning to Data as a Service (DaaS) in the cloud to level the playing field.
Data as a Service and Software as a Service (SaaS) work almost similarly. The two allow clients to access applications delivered using a cloud computing strategy over a network. This saves users the trouble of running the apps locally on their phones or computers. The difference between SaaS and DaaS is that Data as a Service outsources most data processing operations, storage, and integration processes to the cloud.
While the model is proving beneficial to consumers because of the customized software solutions, data specialists should work towards improving the security features of the service. The last few years have seen an increase in remote workers as more people shift towards working from home, which increases their susceptibility to hackers and cyberattacks. Luckily, DaaS is here to help – it can implement stronger security features, such as how login data and passwords are managed. Any cloud service provider should comply with the security regulations from the company.
Large organizations often have a heavy workload. DaaS permits more resources and offers higher scalability almost instantaneously. The model can optimize the data management and processing costs since the model improves the use of the resources for the current workload. DaaS also allows for resource allocation adjustment, which benefits organizations using the cloud service.
Subscribers get several benefits from using DaaS technology. These include:
DaaS makes data storage affordable and offers resources for daily use in business. Because of all the mentioned benefits, DaaS is likely to grow in popularity in 2023. As data engineers and analysts, you should make use of this popularity to better your cloud services.
The most recognizable trend to watch out for in 2023 is artificial intelligence (AI). AI uses machines and computer intelligence to make everyday tasks easier, and it has revolutionized the way things run, from transportation and exploration to data analysis.
Self-driving vehicles and autonomous robot vacuums have artificial intelligence to thank for their creation, as do your favorite voice-activated assistants such as Siri and Alexa. Some areas that have seen massive improvements in recent years include:
Artificial intelligence is improving human abilities at both professional and personal levels and speeding the evolution of data analysis. With the evolution of AI, the market now has a wide array of clever and scalable artificial intelligence and machine learning techniques that deal with small data sets. This is an improvement from traditional AI techniques.
There are several ways artificial intelligence is useful in data analysis and boosting business value. A good example is forecasting customer demands, which consequently boosts sales. AI is also useful in speeding deliveries and warehouse record keeping, increasing efficiency and customer satisfaction.
A good artificial intelligence system is also faster, highly adaptive, and gives high returns on investment. While we can never be sure it won’t give birth to Skynet, we can feel pretty confident AI will be a driving force in 2023.
Natural Language Processing (NLP) is one of the most popular subsections of computer science, artificial intelligence, and linguistics, and it has gained a lot of interest among specialists over the years. In simple terms, NLP mainly focuses on communication between computers and humans using language. NLP empowers computer programs to analyze, identify, and collect huge amounts of data extracted from natural languages. The result is a huge improvement in machine intelligence.
NLP is crafted to read and interpret human languages. It is predicted that NLP will become extremely important in tracking market intelligence in 2023, enabling businesses and other organizations to make sense of their data and additional information to create future strategies. Unlike its predecessors, natural language processing focuses on grammatical issues and sentences associated with the text that makes up the data.
In addition to NLP, artificial intelligence has also led to the creation of automated chatbot processing programs, such as ChatGPT. These automation programs have revolutionized the way people create written content, visual media, and other essential assets for marketing, interaction, data analysis, and more. 2023 will be a banner year for automating content, graphics, and videos with AI tools, as human creators generate new assets with the help of AI across multiple industries.
There are constant changes to data management, and each year brings new developments that alter how data engineers and data scientists store, analyze, and use data. With terms such as “data lakehouse,” “data mesh,” and “data fabric” dominating the landscape, it’s easy to get confused!
Who wouldn’t want to own a data lakehouse? While the term sounds like a wonderful place to relax during the summer, a data lakehouse is a relatively new open data management architecture that combines the flexibility and scale of a data lake with the management capabilities of a data warehouse. By merging the data lake and warehouse, engineers can move faster by using data without needing multiple systems. They can also use TimeXtender to connect multiple data sources, and with the help of machine learning and AI, enjoy better access without the need of the common two-tier data architecture. Expect many data lakehouses to be built in 2023.
Data mesh is an approach to data management that decentralizes the management process and makes it easier for companies to remain agile when it comes to their data. Mesh is integral to help spread responsibilities across an organization, instead of using a top-down or centralized approach that leaves data management in the hands of a few (likely overworked) data engineers or analysts. Keep an eye on this for 2023, because data mesh is likely to be a very prominent data architecture trend.
If data decentralization is your bag, then a data fabric could be the trend that excites you the most for 2023. A data fabric is a type of architecture that allows organizations to store data in various locations, including relational databases, tagged files, flat files, and more. A data fabric doesn’t replace a data lake or data warehouse – it simply helps them operate more efficiently. A data fabric doesn’t care about geography, use case, or deployment – all it wants to do is give real-time access to your important data, which, in the end, is the most crucial aspect of any data architecture approach.
These six trends will be at the forefront of data engineering, data science, and data analysis for 2023, but they won’t be the only ones gaining momentum. At TimeXtender, we’re always excited about what new innovations will be thought of and implemented during a new year, even if they’re just ideas that lead to breakthroughs a decade from now. Which trends do you see leading the way for 2023? Leave a comment, and if you want to learn more about TimeXtender, feel free to get in touch with us at any time.