Data management is not rocket science
If data management isn't rocket science, why does it often feel that way? Due to the complexity, data management often takes a lot of time. Data analysts spend about 45% of their time preparing data. As a result, organizations get far less value from their data investment than they can.
Why is data management so complex and time consuming?
Data is only valuable once it has been validated and prepared. The traditional approach to managing data requires data professionals to spend a lot of time on manual, repetitive tasks that can be easily automated. Performing these tasks traditionally relies on a complex variety of tools, a growing list of data sources and systems, and months of work manually encoding data “pipelines” between three primary components.
- Data Lake: Where you ingest and store all your raw data. The data lake can be used by Data Scientists for advanced analytics purposes using AI and machine learning.
- Data Warehouse: This is used to store aggregated, cleansed, and transformed data for business analytics and intelligence purposes.
- Data Marts/Products: This provides business experts with a subset of data based on their specific domain or use case (customer analytics, sales analytics, financials, etc.), without overwhelming them with a huge data warehouse containing all reportable data.
We call this modern infrastructure the “data estate”. However, we see several problems with this approach to building and managing a data estate.
- Manual coding and pipeline creation: New pipelines must be manually built for every data source, data store, and use case (e.g., analytics reports) in the organization, often creating a vast network of fragile and poorly maintained pipelines. Independent research among data analysts shows that they spend up to 50% of their time solely on these types of manual, repetitive tasks.
- Stack upon stack of tools: To complicate matters further, there are often a multitude of tools for managing each stage of the pipeline.
- Vulnerable Infrastructure: Building and maintaining these complex data infrastructures and pipelines is not only costly and time-consuming, it also introduces security vulnerabilities and governance issues and makes it difficult to adopt new technologies in the future.
- Breakable pipelines: Worse yet, these data pipelines are hard to build but very easy to break. More complexity means a greater chance that unexpected bugs and errors will disrupt processes, corrupt data, and break the entire pipeline.
- Manual documentation and debugging: Every time an error occurs, data engineers must take the time to go through the data pipeline and track down the error. This is extremely difficult if the metadata documentation is incomplete or missing, which is often the case!
Is a Data Management Platform the solution?
The data management market is now full of “platforms” that promise to reduce complexity by combining all your tools into one, unified end-to-end solution. Although this may sound ideal, in practice this is often disappointing:
- Stacks in disguise: Most “platforms” are really just a stack of separate tools for building and managing each piece of the data estate bundled together.
- Tools bought together: Tools are often sold by the same vendor, but they are often collected through acquisitions. This often results in a jumble of incompatible code that has been merged into a “platform”.
- Low-code: Many platforms brag about being “low-code”. When you dig into the details, there are usually only 1 or 2 features that actually have this functionality.
- Welcome to data governance prison: Worst of all, you’ll end up locked into a proprietary ecosystem that doesn’t allow you to own, store, or manage your own data. All tools and processes are predefined by the platform developer and then hidden in a “black box” that you can’t access or change. Many of these platforms even force you to migrate all your data to the cloud and don’t support on-premises or hybrid approaches.
- There's no escape: Not only do these platforms significantly limit your data management options, if you later decide to migrate to another data platform, you'll have to rebuild your data infrastructure from the ground up.
These solutions are not real "platforms" and they certainly do not "unite" anything. They are tool stacks with many limitations.
A new approach to winning in the machine economy
We believe that you shouldn't be forced to manually code fragile pipelines for months and rely on a collection of disjointed tools. We also don't believe in poorly integrated “platforms” that impose strict controls and lock you into an ecosystem of your own. It is clear that these old approaches to data management simply do not meet the needs of modern data teams. The fast pace of the machine economy does not take into account the bottlenecks, delays and limitations associated with these traditional approaches. Data professionals need a faster, smarter, and more flexible way to build and manage their data domains.
The future of data management is low-code, agile and integrated
According to Gartner, by 2024, 75% of all applications worldwide will be built using low-code development tools. In fact, 41% of employees outside of IT already develop their own data and technology solutions using low-code “builders”.
Shopify, Salesforce App Builder and Microsoft Power Apps are some of the most well-known examples of these tools. In an ideal world, data professionals would be able to ingest and prepare data with their own low-code solution, allowing organizations to turn raw data into actionable insights faster than ever before. To meet the challenges of the machine economy, however, data professionals need a solution that goes beyond low-code. It must meet all three of the following criteria:
- Low-Code: It should be smart enough to build all available data for you by automatically generating all underlying code and documentation, from start to finish.
- Agile: It should provide both technical and business users with a simple user interface.
- Integrated: It should seamlessly integrate your data infrastructure into an easy-to-use, metadata-driven platform.
Meet TimeXtender, the Low-Code Data Estate Builder
TimeXtender enables you to build data warehouses up to 10x faster with a simple, drag-and-drop solution for ingesting and preparing data. Code and documentation are automatically generated, reducing build costs by 70%, freeing data teams from manual, repetitive tasks, and enabling BI and analytics experts to easily create their own data products. TimeXtender seamlessly wraps your existing data infrastructure, connects to 250+ data sources, and integrates all the powerful data preparation capabilities you need in a low-code, flexible, future-proof solution.
The benefits of TimeXtender for the different users at a glance:
- Business managers gain rapid access to reliable data, with 70% lower build costs and 80% lower maintenance costs.
- Data teams are freed from manual, repetitive tasks and have more time to focus on higher-impact analytics projects.
- BI and analytics experts get a code-free experience for creating their own data products – no more bottlenecks.
Want to learn more about TimeXtender and discover how you can set up and maintain a modern data platform 10x faster, become more data-empowered and win in the machine economy?
TimeXtender Workshop
Do you want to handle your data more efficiently as a company? Do you want more insight, better reporting and faster decision-making? Then our TimeXtender workshop is exactly what you need!
