The demand for timely and contextual data will never slow.
It might be time to rethink your data strategy
The burden to support your current data architecture will only continue to grow as the demand for data increases. Without an effective strategy to meet this demand the costs and effort will reach a breaking point.
The organization must therefore develop a data strategy that can support this growing demand.
Getting in the way are legacy technologies and legacy processes and the costly data pipelines required to support them.
A modern, enterprise data strategy is one that addresses both database sprawl and stovepipe ETL in order to reduce costs and increase overall agility and transparency.
Architecture Challenges We Solve
What is database sprawl?
Database sprawl is the unchecked growth of individual silos of data across an organization. This sprawl is due to the continuous demand for specialized data. IT often reacts to this demand by creating new, one-off datasets. Over time, the number of data sets can count in the thousands. This creates a tremendous burden on IT.
What is stovepipe ETL?
Stovepipe ETL occurs when IT creates a new extract, transform, and load (ETL) process for the purposes of creating and maintaining a specific database. They are called stovepipes because each process is specific to the needs of that particular use case.
Overtime, organizations find themselves managing an unwieldy "forest" of these stovepipes.
Problems with Traditional Data Architecture
The traditional data architecture requires a number of specialized tools, platforms, and subject matter experts in order to function. As data grows, the burden on this complex environment grows with it. This complexity prevents decision makers from getting the information they need in a timely manner and therefore creates additional lost opportunity costs.
"Process Tempo gave us the data platform needed to consolidate our infrastructure and make our team more agile."
Solving this problem
To solve this problem requires organizations to implement both a simpler architecture and a means to offer self-service access to data in a controlled fashion. In addition, this architecture must encourage users to keep data within the platform. All to often, users request extracts of data and once data is extracted, the organization is exposed to greater risk. This extracted data can not only land in the wrong hands, once it is extracted, it ages rapidly. Making decisions using this data can lead to challenges for the organization.
The Process Tempo Approach
Process Tempo offers a modern alternative to traditional data architectures. Implementing Process Tempo will reduce the need for stovepipe ETL, reduce database bloat, and help the organization maintain greater control of its data. This equates to less cost and less risk.
Leveraging existing ETL capabilities, organizations can land data into a single instance of Process Tempo. This is possible because of Process Tempo's No-SQL approach and its flexible schema. The organization no longer needs to plan out the design and implementation of a specialized data warehouse.
In addition, Process Tempo provides built-in features to allow decision makers direct access to data in a secure and controlled fashion. By offering data consumers self-service access to data, the organization will benefit from reduced processing costs and faster time to insight. This translates to a more agile and transparent organization.
Finally, Process Tempo provides built-in features such as search, analysis and reporting. These features are designed to keep the data within the platform which means greater control, greater security, less risk, improved audits and a whole host of additional benefits.