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Prediction 1: Data catalogs and other data management tools will become obsolete unless part of a larger platform play

A number of years back, Gartner proclaimed that data catalogs were 鈥渢he new black鈥� when it came to data management and analytics. It鈥檚 not a new concept per se; there鈥檚 long been an understanding that you need a common definition of all your business terms, goals, data sets and systems. But what we鈥檙e seeing is the move beyond simple taxonomies to ontologies, which is really understanding the classes of relationships and the structure of data and rules 鈥� and how you establish constraints around how the data is managed. It鈥檚 more than just a classification. Companies have been implementing these things in pockets, but it鈥檚 often in siloes and not spread across the lines of business. While useful independently, all of these things are much more powerful when looked at as a whole. 

For this reason, individual tools that only perform one function will not be enough to solve the growing challenges in data management. Even the definitions and criteria used by analysts to classify the leaders in the industry are rapidly changing. Words like augmented, discovery, suggested, etc. are being put in front of classic categories like data quality, integration, master data management and others, to further highlight the requirements and connected nature of these products. Expect to see long-standing leaders falling behind and innovators taking the lead this coming year. 

Prediction 2: More organizations will move from a data mesh architecture to a data fabric approach

Data mesh architecture is a strategy of decentralizing the data 鈥� with a goal of establishing coherence between different business domains. It鈥檚 primarily people driven. It tends to involve subject matter experts in data who are going in, tagging information, designing rules and understanding who the contributors are, to try to create a better awareness of how to consume data at a business level or what鈥檚 happening within your data that may be impacting your business. 

By contrast, a data fabric approach is more automated. It鈥檚 going to use AI/ML and it鈥檚 not reliant on highly experienced data scientists. It鈥檚 going to be more self-discovering in terms of the insights and value it creates. This design pattern is going to be increasingly important for companies to start adopting and that鈥檚 definitely the direction I see things headed. If you鈥檙e not moving toward an automated approach, you鈥檙e not going to be able to keep up and you won鈥檛 be able to unlock all the potential value in your systems and data. 

Prediction 3: Cloud data management takes on outsized importance

There is way more opportunity for businesses seeking to eliminate legacy  data management products now that more solutions are becoming cloud-native. They鈥檙e designed for extensibility and integration and create opportunities to build a 鈥渂est of breed鈥� solution set that matches the exact needs of the customer. 

When we think about all of the individual parts 鈥� cloud infrastructure, integration, data lakes, analytics platforms, etc. 鈥� customers increasingly want to look at how to combine these functions between strategic application providers and their cloud platform providers.

For example, a customer who has made the decision to move their IT infrastructure to the cloud will want to select a cloud provider that offers not only compute and storage, but data management solutions and services that will accelerate their modernization efforts. These services need to be looked at in terms of how well they will interoperate with the core business applications that will run on that cloud platform. The ability to extend core applications with rich functionality beyond infrastructure, will ensure the move delivers the highest business outcome possible and not just an exchange of CapEx for OpEx. So, platform, applications and tools that can accelerate this integration and expansion 鈥� or help make it faster and easier 鈥� will be in higher demand. 

Prediction 4: Demand for data observability will grow

When it comes to data and data migration, there are a ton of moving parts. There may be multiple application vendors and multiple cloud providers. To grapple with all this, you need a better understanding of the health of the data in your systems and the impact that health, good or bad, has on your business. Data observability helps you assess data health and the ability to troubleshoot and fix problems before things get worse. 

The concept actually comes from the notion of DevOps and the ability to remove the silos between development and IT operations for collaborative, fast and iterative product delivery. Data observability basically steals a page from the benefits of DevOps and applies it to mission critical, corporate data.

Data observability is a measure or approach to help an organization really understand how well their data is doing in supporting the business. 

If you鈥檙e not empowering your teams to truly understand the impact data is having on their business, and the tools to address those issues, you鈥檙e going to be inefficient, and you鈥檒l be spending more than you should. That鈥檚 why I see this as being a growing trend for 2023.

Prediction 5: Customers will become more demanding of their vendors to be able to prove tangible business impact 

If all you can talk about are software features and how your features compare to another product鈥檚 features, then you鈥檙e not delivering any value to your customer. You must be able to show the business impact that software will have on the business, and the ROI associated with its implementation. You need to be able to relate it to the things that the customer鈥檚 board and the CEO are using as key metrics. If you can鈥檛 draw a clear line to real value, you鈥檙e not going to sell your software.

In many conversations I鈥檝e had with executives at the companies we serve, I often hear 鈥渨e already have one of everything so tell me why this time things will be different.鈥� The promise of 鈥渢hings will be great, trust us鈥� is not going to cut it, especially given current economic uncertainties. However, when faced with economic uncertainties, most companies will look at solutions that help cut costs, improve efficiency and streamline business operations. This is why showing tangible business impacts will be a cornerstone in most enterprise software buying decisions.