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Technical Capabilities

At the center of all Process Tempo's offerings is a demonstrated ability to alleviate bottlenecks and unique pain points in Data’s Path-to-Value process. Our Managed Services augment existing systems by leveraging an organization’s available tools & technologies to:

 

  1. Enable faster and simpler access to trusted data

  2. Strategically analyze, predict, and achieve quantifiable results

  3. Quickly adapt to changing business conditions

  4. Leverage big data technologies on the cloud

  5. Deliver continuous improvement to an organization's data analytics efforts

  6. Reduce the total cost of ownership (TCO).

Learn more about how we provide these features through our available solutions below.

 
Agile Delivery: Implement data driven projects using the latest agile methodologies

The more agile an organization, the better they are able to adapt to changing conditions, react to internal and external changes, and identify, implement, and push through vital projects quickly and with confidence. These sought-after results can be achieved on a team and project level by implementing the Process Tempo agile delivery methodology: a robust, continuous process of planning, executing, evaluating, and improving.

The Process Tempo Value-Driven Approach

The Process Tempo approach to agile utilizes a methodology that supports The PTCD Model (The Process Tempo Continuous Delivery Model) that is tightly coupled with the needs of our clients. This means that the development of analytical deliverables is highly transparent to the stakeholders, and top-level engagement helps to dictate priorities and monitor progress.

 

Process Tempo understands that the success of agile is dependent on strong, cohesive teamwork and collaboration, and makes a point to highlight and strengthen this area, ensuring that everyone is making fully-informed decisions based on actionable data, information, and recommendations.

Benefits include:

 

  • Stakeholder Engagement

  • Transparency

  • Early and Predictable Delivery

  • Predictable Cost & Schedule

  • Change Management

  • Focus on Business Value

  • Quality Improvement

The ability to realize faster processes means that data updates can be provided within seconds, making it possible to use and manage data volumes more effectively. It allows for teams to catch errors immediately, and flag incorrectly processed data before it is passed downstream and analyzed in a production setting. This enables teams to focus on their own areas of expertise by removing the burden of poor quality and inefficiencies in the process. It's critical to use agile methodologies to spur competitive advantages, fuel business innovations, and enable adaptability in fast-changing environments.

Although Agile projects are 28% more successful than traditional ones, they still have a level of risk that can be addressed by having the right controls in place to help realize business value, reduce the risk of building the wrong product, and increase overall development success."
 
Data Ops: Day-to-day overall management and oversight of the use, production & consumption of data

Data Ops is a process-oriented methodology used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics.

 

More modern Data Ops processes are now harnessing the power of accompanying modern data platforms to take Data Ops one step further.

Different groups within the organization may have their own process when it comes to dealing with data, but establishing an overarching methodology like Process Tempo’s can help provide a more efficient organization while simultaneously improving individual and group performance.

 

The purpose of the platform + service combination is to help the organization actively manage and improve the data ops life cycle and the specific criteria associated with it. Process Tempo's approach to Data Ops allows for the design of highly specific models and architecture, enables rapid prototyping prior to implementation, and makes it incredibly fast and easy to put wireframes into production. 

Data quickly becomes easier to understand, to ingest, to clean, prep, and test - with each stage showing marked improvement and delivering insight back to the business that is fast, digestible, and impactful. The ability to realize faster processes means that data updates can be provided within seconds, making it possible to use and manage data volumes more effectively.

It allows for teams to catch errors immediately, and flag incorrectly processed data before it is passed downstream and analyzed in a production setting. This enables teams to focus on their own areas of expertise by removing the burden of poor quality data and process inefficiencies. Ultimately, the approach helps spur competitive advantages, fuel business innovations, and enables adaptability in fast-changing environments. 


Advantages include real-time data insights, improved collaboration among various teams in an organization, quick and effective response to new requests, better operations and support, real-time goals for the organizations, avoidance of disastrous scenarios by predicting them in advance using data analytics, improved efficiency and overall quality through statistical process control (SPC), and shortened time to fix bugs and defects.

  • Faster process: Get data updates within a matter of seconds and make it possible to manage and use increasing data volumes effectively while reducing the cycle time of data analytics.
     

  • Real-time insights: Speeding up the entire data analytics process to close in faster on real-time insights in your data. In the fast-changing world, the ability to adapt to any market changes is critical. DataOps moves code and configuration continuously from development environments into production, leading to near real-time data insights. 
     

  • Focus on import issues: With the time-savings and more accurate data analytics, data teams can now focus on market needs and changes as they occur. DataOps allows IT leaders to focus more on improving communication, integration, and automation of data flows enterprise-wide. Without the burden of inefficiencies and poor quality, teams can focus on their area of expertise; creating new models and analytics that fuel business innovation and create a competitive advantage.
     

  • Catch errors immediately: Output tests can catch incorrectly processed data before it is passed downstream. Tests ensure the reliability and quality of the final output by verifying that work-in-progress (the results of intermediate steps in the data pipeline) match expectations.

"Backend data teams are strapped for resources. It's why 73% of organizations plan to invest in DataOps... it is as much about people as it is about tools and processes."
 
Infrastructure Design:
Design, implement, and maintain an architecture that support a wide range of data & analytics use cases

Having the right insights at the right time can separate an organization from competitors and provide unprecedented opportunities for growth. A well-established, well-functioning data infrastructure is instrumental to ensuring these opportunities arise for organizations seeking to get more from their data. 

 

The most common challenge when developing sound data infrastructure appears at the beginning of the process: in identifying how to initially model data to fit the unique needs of the organization and to meet the individual needs of data analysts and scientists.

 

This task typically falls to IT to create a structure around storing data - in tables, columns, or indexes - and is based upon how that data will later be retrieved and used down the line. Often, the desired use gets lost in translation, and analysts on the receiving end are met with incorrect formats or unusable data that lacks context.

A well-functioning data infrastructure cannot exist in modern data environments without the ability quickly and easily bring data together from disparate sources in order to solve particular problems. Process Tempo can design your internal data infrastructure to best fit the unique needs of your organization, with focus on flexibility and adaptability to make your data infrastructure a long-term, lasting-impact solution.

"Our process increases the success rates of strategic projects, increases the alignment and focus of management around strategic goals, clears doubts for the operational teams when faced with decisions, and builds an execution mindset and culture."
 
Data Governance: Collect, organize, access, and enrich metadata to support data discovery and governance

Ever-growing, complex data environments typically create organizations that are consistently behind the curve and are actively failing to unlock the full value of their data. Analysts, data scientists, and nontechnical business users are incapable of doing their jobs effectively because they cannot easily access accurate and reliable data for their data-driven initiatives. Overall, there is a significant amount of time spent searching for the "right" data than there is analyzing and making informed decisions.

 

Once the data is found by users, more time is dedicated to verifying that the data can be trusted - oftentimes, it can’t be; there can be significant knowledge gaps regarding where data comes from, what it holds, and whether it even answers the question that teams are seeking answers for in the first place.


This struggle to find data, paired with the inability to trust it, not only directly impacts day-to-day users but ultimately causes the entire organization to suffer. The current approach for navigating the modern, complex data environment is actively leading to poor and inaccurate business decisions, is slowing innovation, blocking growth, and preventing a competitive advantage.


A renewed approach to navigating the modern, complex data environment will be extremely beneficial to organizations seeking to gain a competitive advantage. Implementing the Process Tempo approach to data governance can help users find the data that matters, granting them the ability to ‘shop’ within their own internal resources for the trusted data they need.

Even once the data is found by users, more time is dedicated to verifying that the data can be trusted - oftentimes, it can’t be; there are significant knowledge gaps regarding where data comes from, what it holds, and whether it even answers the question they were seeking answers for in the first place. 

This inability to find data, paired with the inability to trust it, not only directly impacts day-to-day users, but ultimately causes the entire organization to suffer. The current approach to navigating the modern, complex data environment is actively leading to poor and inaccurate business decisions, is slowing innovation, blocking growth, and preventing a competitive advantage.

A renewed approach to navigating the modern, complex data environment will be massively beneficial to organizations seeking to gain a competitive advantage. Implementing the Process Tempo Data Catalog helps users find the data that matters, granting them the ability to ‘shop’ within their own internal resources for the trusted data they need.

"The average financial impact of poor data quality on organizations is $9.7 million per year."
 
Pipeline Development: Develop, implement, and facilitate the flow and transformation of data from source to consumer

ETL stands for Extract, Transform, Load - three database functions combined into one tool that pulls data out of one source and places it into another. Extract is the process of reading data from its source and collecting the data. Transform is the process of converting the extracted data from its previous form into the format required or requested. Load is the process of writing this data into the target source.


ETL is a highly significant element of today’s BI systems, allowing for disparate data sources to be blended, programmatically analyzed, then displayed as broader, more contextual business insight. Despite having a pipeline, organizations are spending significant time - weeks, or even months - in the data manipulation phase, improving data quality, integrity, profiling, and correcting inconsistencies.

 

The technology available within the Process Tempo platform, coupled with our streamlined approach, cuts these processes to mere hours. This significant reduction in time frame stems from new and improved visual flow technology, a capacity to leverage existing development frameworks, and a refined ability to track data lineage and perform impact analysis. Process Tempo allows organizations to reallocate their time to focus more on reviewing dashboards, reports, insights, and intelligent business decision-making.

After choosing data sources, can Process Tempo help to automatically identify the type and format of the data, sets the rules how the data has to be extracted and processed, and loads the data into the target storage, taking out the traditional coding effort required.

Built-in error handling functionality to help data engineers develop a resilient and well instrumented ETL process. Great for complex data management situations and moving and transferring large volumes of data in batches. Assist with data analysis, string manipulation, data changes and integration of multiple data sets, as well as advanced data profiling and cleansing.

Improved access to information directly impacts the strategic and operational decisions that are based on data driven facts. It enables business leaders to retrieve information based on their specific needs and make decisions accordingly. High return on investment (ROI) helps business to save costs and thereby, generate higher revenues.

Out-of-the-box performance enhancing technologies, including cluster awareness applications designed to call cluster APIs in order to determine its running state, in case a manual failover is triggered between cluster nodes for planned technical maintenance, or an automatic failover is required, if a computing cluster node encounters hardware

"The implementation of ETL, on average, results in a median 5-year ROI of 112% with a mean payback of 1.6 years""
 
Continuous Improvement:
Continuously improve internal data processing and analytic efficiencies and capabilities

In the modern workplace, knowledge and technology can quickly become outdated, obsolete, or simply fail to serve the organization at the level required. Teams are locked into specific tools that limit their ability to adapt, collaborate, or innovate. The Process Tempo platform approach removes those limitations and enables space for an established Continuous Improvement (CI) methodology.


Process Tempo’s Continuous Improvement offering can help prevent organizations from dipping below required levels and ensure the ongoing improvement of products, services or processes through ongoing incremental and breakthrough improvements.


It gives organizations a framework to reach the next level of excellence, helping to increase the value of products and services for the client and leading to more sophisticated and overall more economically competitive offerings.


There is maintained focus on identifying the golden areas within datasets and relationships. The use of relationship analytics allows organizations to hone in on aligning products and services to internal and external needs and on minimizing waste in the value delivery process. This leads organizations to create and maintain products and services that can better “anticipate” these needs.
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Benefits:

  • Increased productivity

  • Greater agility

  • Improved quality

  • Lower costs

  • Decreased delivery times

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Performance Monitoring:
Continuously track the health of your data and analytics processing

Performance monitoring and analysis are critical to deciphering the often complex behavior of parallel applications. The smallest outage, downtime, or even temporary drop in an important metric can have cascading impacts on an organization.


Lack of clarity in these situations, along with an incapability to both quickly and accurately address and rectify such issues, can have a significant impact on critical business functions like cost, dependability, and performance.
Due to the intricacies of today’s systems, the capacity to deal with these issues in a timely, impactful manner are decreasing with each added layer of complexity - whether that complexity is caused by people, process, or technology.

 

With the Process Tempo platform, and with our approach to Performance Monitoring, we significantly boost an organization's capacity to deal with outages, downtimes, and general performance issues by bringing transparency to an overly complex environment.Generating a clear overview of respective systems helps to eliminate inefficiencies and identify efficiencies in their place. It can rapidly improve response times, enhance predictability, and encourage proactiveness by identifying areas that may otherwise be overlooked.

"By 2023, most infrastructure and operations organizations deploying cloud provider and software monitoring technologies will experience at least a 35% reduction in IT monitoring costs.”
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Decision Modeling & Intelligence: Map out detailed requirements of unique, individual use cases, then organize and design models to achieve maximum output efficiency

Decision-Modeling and Intelligence brings together a number of disciplines including data and business modeling, decision management, and decision support. It is the process of mapping business requirements to the overall objectives of the organization, and where data operations meets data strategy.

 

Process Tempo's unique approach to use cases takes one particular concept into account: that individual use cases cannot be created in a vacuum. Does the effort to develop this use case fit current priorities? Does the effort produce a return on investment (ROI)? Does the effort leverage existing capabilities or require new ones? Can this use case be designed to roll out across new or existing business units?


Use case design begins with a data model. A data model organizes data elements and standardizes how the data elements relate to one another. Since data elements document real life people, places, things, and the events between them, the data model represents the reality of the use case design.


There are some misconceptions about data modeling that are important to address. Some perceive it as simply as documentation, as a bottleneck to development, or even as too expensive to be worth it. Realistically, it has been demonstrated time and again that data modeling accelerates development, significantly reduces maintenance, increases application quality, and lowers execution risks across the enterprise.


Benefits:

  • Higher application quality

  • Quicker time to market

  • Lower development & maintenance costs

  • Improved data quality

  • Better performance

  • Documentation & knowledge transfer

 

 
Executive and Operational Dashboards & Reporting: Augment existing capabilities with state-of-the art dashboards and reports

Getting actionable insights from your data can be a challenge depending on the Volume, Variety, Velocity, Veracity, and Value of your data sources. Large disparate data sets can be difficult to blend and identify relationships. Add in streaming and/or ‘dirty’ data, and the challenges just grow, especially with hand-coded solutions that do not scale easily.


Typically, an organization’s data requires several steps to ingest, blend, cleans, normalize and prepare before insights can be derived. The final step usually has data presented in reports, visualizations or dashboards.


Unfortunately, if the data is not accurate or relationships are not correctly identified, it doesn’t matter how ‘flashy’ the chart is, because you are not visualizing trustworthy data. 


Process Tempo provides a modern platform that allows Data Engineers, Data Scientists, Business Users & C-level executives the ability to manage the entire data pipeline from source data to compelling reports and dashboards.


With Process Tempo, customers are gaining never before seen insights from their data. Built on Graph Database technology, Process Tempo allows our customers to model, import and analyze their data all within an intuitive, graphically rich user interface. Relationships across datasets can automatically be identified and interacted with, allowing all data consumers to drill deep into datasets to find those ‘golden nuggets’ within your organization. Process Tempo’s customers have access to an extremely agile and scalable platform based on graph technology.


With our intuitive interfaces and strong data integration, our customers have reduced development times from weeks and months, to hours and days.
Process Tempo provides customizable reports and charts right out-of-the-box. However, if you are already using a BI tool such as Tableau, Qlik or PowerBI, Process Tempo can assure the data is accurately prepped for visualizing in one of these tools.

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