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Top line growth, bottom line savings

  • Introduction

  • How To Determine If Analytics as a Servicce Is Right For Your Organization

  • The Common Challenges of Fielding an Internal Analytics Team

  • Advantages of Outsourcing Analytics

  • What's New

  • The Must-Have's of an Effective Analytics Service in 2021

  • Next Steps



Business organizations are prioritizing data-driven decision-making as a means to keep pace with their competitors and the market. While a difficult enough challenge in itself, factors like the pandemic, increased global competitiveness, and the collective focus on digital transformation have all individually accelerated the demand for higher quality insight.


Insight has analytics at its core.


As a result, these elements have created immense pressure on data and analytics teams to operate above and beyond what they're capable of. These teams are understandably struggling across the board.


Analytics teams are faced with extremely complex data environments and cloudscapes that are quickly growing in size and cost. These intricate webs of systems, applications, policies, and bureaucracies are becoming even more of a challenge to navigate given the limited amount of skilled resources and personnel available to help untangle these webs.

The amount of data is also growing significantly. International Data Corporation (IDC) has estimated that the total amount of data created worldwide is expected to grow by 163 zettabytes by 2025, with the amount subject to data analysis to grow by a factor of 50.

Even the strongest in-house data and analytics teams are currently facing a steep backlog of analytics that they owe back to the business.

So where do Managed Analytics Services (MAaas) come in?


In the same way that companies shifted resources to the cloud, more companies are shifting to Managed Analytics Services to help alleviate these difficulties, direct more of their energy (and budget) into improving products and services, and leverage teams to make intelligent and profitable decisions - all without a heavy, upfront investment.


In 2021, the organizations that begin the process of outsourcing managed analytics efforts will find themselves better suited to focus on process improvement and other value-add activities, and this trend will continue in the coming years.


From Gartner Predicts, 2020:



How To Determine If Managed Analytics as a Service (MAaaS) Is Right For Your Organization


To many, an organization’s data is its lifeblood. Knowledge of this data is key to an organization’s ability to compete and succeed. Can an organization, therefore, outsource such a critical part of its decision-making process?


The answer is a resounding yes, and there are a number of factors that will indicate whether your organization should consider making the switch. But it is critical that organizations first ask certain questions within the context of their own operations to determine if Managed Analytics is right for them.


To determine if it is sensible to outsource this type of work, organizations should start by asking themselves the following (Answering "yes" to one or multiple indicates that Managed Analytics should be on the table as an option to implement sooner rather than later):


  • "Are any of our efforts surrounding data repetitive in nature or can be considered a commodity activity?"

It's important to know that the effort to procure and process data is often the same across multiple use cases. In this context, it is likely that some of this work can be deemed a commodity activity and therefore can be outsourced to a Managed Analytics Service.


  • "Are we investing a lot of time into gathering, organizing, and analyzing important data?"

If it feels like the answer is yes, it's important not just to address this problem, but also to acknowledge that other areas of the business are likely suffering as a result. 


If just five employees spent one hour less daily on gathering, organizing, and analyzing data, the organization gains back a collective 120 hours of valuable time on a weekly basis. Over the spread of a single year, and between even more employees, the hours wasted on work that could be easily outsourced rises comfortably into the thousands. 


Fortunately, Managed Analytics Services are typically up-to-date on best practices on how to quickly gather data, turn it in contextual information, and make that information easily accessible and searchable. A Managed Analytics Service should also be able to provide advanced analysis capabilities for analysts. 


By providing a mix of technology and expertise, Managed Analytics Services can streamline and rapidly speed up the process of gathering and organizing data, returning countless hours of valuable time that can be better spent on move-the-needle projects.


  • "Are we finding it difficult to make use of the data that we collect - much less turn it into actionable, impactful insight?"

The most impactful insights are often generated from patterns and relationships that naturally exist within a companies data. However, these patterns and relationships will be largely hidden from view if the bulk of a company's data is not connected or linked. 


If there is no real transparency into how different areas and data within the business interrelates, it's incredibly difficult to recognize the kinds of patterns and relationships that can drive a business forward.


An effective Managed Analytics Service will implement some form of Graph technology in order to ensure data is correctly linked together. Without Graph technology, bringing data together in a way that provides context to the data and clearly shows patterns and relationships is incredibly difficult. 


It takes a skilled team to set up graph technology that can work quickly and effectively for your business. Make sure your Managed Analytics Service doesn't just bring graph technology to the table, but can also demonstrate exactly how it's going to fit the nature of your business and start providing value as quickly as possible.

So what should organizations keep in-house?

What organizations need to keep in-house is not so much how to store and transform data, but rather the deep understanding of which metrics to collect and analyze.


Businesses should consider reorganizing their internal teams to focus on defining metrics and understanding the data model needed to calculate and display these metrics. This initial focus on metrics is essential, as outsourced data and analytics should not come without a strategy that ties into the objectives of the business as a whole.


A Managed Analytics service can help define the strategy and augment the approach to providing those metrics. Once the strategy is defined, the mechanics of data prep, storage, etc., can easily be outsourced in total alignment with the strategy.


NOTE: The most effective methodologies and strategies provided by managed analytics services should add capability without adding layers of additional complexity. 


Common Challenges with Fielding an Internal Analytics Team


Collectively, enterprises are predicted to spend over $3.7T on Information Technology in 2021 with an increasingly larger portion of this spend on cloud-based applications and services. While shifting dependency to the cloud is a sensible move, the elephant in the room is the stubborn presence of legacy systems and the complex web of processes and procedures. The analytics team has to operate in the sandbox they have been provided, which means being faced with a number of challenges.

Challenges associated with staffing, workloads, and processes:

  • Developing an internal team with the required broad set of skills can be a very difficult, costly, and time-consuming process. World-class analysts are in high demand and the market is seeing an acute shortage of available talent. 60 percent of businesses believe it is harder to source talent for data and analytics positions than for any other roles.

  • The constant and increasing demand for analytics-based insight generates a growing backlog of unmet deliverables. Internal teams are often unable to keep up with an increasing number of analytics requests.

  • Organizations have adopted platforms that scale data and data processing but have yet to find ways to scale the subject matter expertise required to produce insight.

Challenges associated with the enterprise's existing set of tools:

  • Data and analytics architectures are littered with individual tools and platforms, creating a lack of cohesive architecture and causing an undue complexity that is incredibly difficult to navigate and manage.


  • Direct data pipelines complicate the issue in that they are produced en masse for single requirements and with little to no reuse.

  • System-generated data often needs to be blended with people and process-based data to create contextual insight for decision-making, meaning that relevant datasets need to be matched together. This matching or blending process continually proves difficult for many in-house teams.


  • Feedback mechanisms to improve data are not often implemented as part of a continuous improvement (CI) effort as current data architectures do not support this capability.


The Advantages of Outsourcing Managed Analytics

Avoiding the common pitfalls of internally-run analytics efforts is a strong enough selling point in itself. But there are some other major benefits to outsourcing Managed Analytics: 



Arguably the most significant benefit of outsourcing Managing Analytics is the cost. Outsourcing Managed Analytics translates to access to highly-specialized, senior analytics resources at a much lower cost than hiring and onboarding internal dedicated teams/departments.


Managed Analytics provides the freedom for both technical and people resources to be easily scaled upward or downward to meet needs as they arise, and provide the skills upfront that are best suited for each individual project or use case. The focus here is key: on augmenting existing operations vs. replacing them outright, avoiding any potential disconnect or wasted “transition” time. 


With in-house teams, competing internal priorities often create lengthy timelines for deliverables. Dedicated outsourced teams enable projects to be delivered within far faster timeframes.


Organizations can enjoy the availability of pre-packaged templates for particular industries, business processes or for integrating with various data sources to help speed up the development cycle, as well as leveraging learnings from past deployments.


Specialized managed analytics services are more up-to-date on the latest evolving technologies and techniques, giving outsourced teams the ability to outmaneuver industry competition.


The organization gains access to a wealth of experience across different industries as well as knowledge of similar businesses and projects that can add additional value.


Both management and executive teams are granted the freedom to focus on other core operations of their business.


What's New in Managed Analytics?

Shorter Time-To-Value:

Timely insights are essential in the face of constant change. The role of data and analytics teams is not only to turn data into insight but to present that insight at the point of highest impact. This helps organizations be decisive in the moments that matter most and capitalize on them while limiting missed opportunities.


In the current climate, data and analytics teams have significantly deep backlogs that delay their response to any single request, with delays commonly amounting to months at a time. With Managed Analytics, actionable insights are available within hours to mere minutes.


Shifted Focus to Scaling People, Not Just Data:

The latest cloud technology allows scaling for data processing, but bottlenecks in data’s path-to-value process still exist despite this development. Bottlenecks stem largely from people who have to interpret, design, analyze, and report on data.


Newer software is designed to specifically address those bottlenecks by making data more accessible, organized, powerful, and easier for people to interact with and use. This accelerates the ability of an organization to get the right data in the hands of the right people at the right time. It also has the added bonus of helping to boost data literacy skills for the casual user.


Updated, Customized Approaches:


Depending on the needs of an organization, different approaches can be taken in order to maximize the value of both the service itself and the returns for organizations. These approaches can change based on elements like the current state of an organization's data and its underlying business objectives, for example.


Accompanying Core Capabilities:


In conjunction with a platform, modern-day managed analytics services implement a number of core capabilities and accompanying best practices to help augment existing data and analytics efforts even further. This helps to reduce the backlog of current analytical requirements while simultaneously fostering an analytics culture within the organization.


The Must-Haves Of An Effective Managed

Analytics Service In 2021

In order for a Managed Analytics Service to be truly effective, it realistically should meet certain criteria that span across people, processes, and technology, and the business overall. The following is a checklist of these criteria to look out and shop for when evaluating Managed Analytics Services.

  • Core Data Architecture & Strategy

  • Accompanying Methodologies

  • Accompanying Platform

  • Accompanying Capabilities

Core Data Architecture and Strategy

Enablement of Self-Service Reporting & Dashboard Creation

-  Flexibility in connecting to existing data tools and platforms

-  The ability for users to access and interact with data directly

-  Easy for users to generate and share reports

-  Ability to create dashboards unique to individual requirements

-  The ability for remediation

An All-In-One Cloud/Hybrid Capability

-  Ability to scale to meet the demands of a variety of use cases with solutions that can be rolled out across additional business units

Rapid Deployment & Onboarding

-  No coding or extensive training required

Industry Knowledge & Support

-  Ability to get the support needed from industry experts, that will ask the right questions when it comes to your most significant challenges - and find the right answers

Accompanying Methodologies

Industry Recommendation
The “Combination” Approach, or the “Three-Pillar” Approach:
Data Strategy + Data Governance + Data Design


Combining each of these elements demonstrably provides a comprehensive approach to ensure all known and unknown elements of an organization’s data and analytics requirements can be met in both the short and long term.

Data Strategy:

Aside from improving the quality and accessibility of trusted data, implementing a modern data strategy can help align data initiatives with organizational strategy, enabling business and technical partners to work more in sync with one another toward their goals.

Data Governance:

Ensure that managed data is both governed and controlled, yet still flexible for users and for timely decision-making purposes. Balance the needs of the organization with the needs of the typical user, all while adopting the process to increasingly cloud-ready environments.

Data Design: 
Ensure that carefully crafted deliverables and artifacts are available to allow both technical and non-technical users alike to intuitively traverse massive datasets, understand the connections present between them, and easily share that knowledge between peers.

Accompanying Platform


"Companies that master how to apply their data are creating the most wealth. 5 of the top 10 Fortune 500 companies are platform firms. Managed Data & Analytics will be one of the most important services, and, at a top function, leading to the success of next-generation companies."


It is critical to onboard platform technology in conjunction with a Managed Analytics offering. Platforms help significantly reduce the complexity of data environments and maximize the benefits of a Managed Analytics Services’ accompanying methodologies & capabilities. 


The Necessary Platform Features Your Managed Analytics Service Should Provide:


Cloud-Ready Data Warehouse:

This element should be designed for both technical and non-technical users. It should be able to enhance functionality, flexibility, and scalability, all while reducing cost. Like Google™ for your business, searchable data should support easier access to information. Admins should be able to centrally monitor the use of data from a single catalog interface.


Self-Service Data Access:
Allow and manage data access for those who need it, where they need it, and exactly when they need it. No more wait times. Ability to import data from multiple, disconnected datasets into a single, central platform that everyone can use. Manage security and entitlement from a central administrative console. Onboard and off-board users with ease. Get projects off the ground faster.


Advanced Analysis Capabilities:
Out-of-the-box analytics solutions to create instant insights that can be extended to include 3rd party curated analytics created by user communities. Built-in, no code report builder should integrate with other platforms. Built-in analysis features should be able to empower even your least-technical user.

Accompanying Capabilities

  • Agile Delivery

  • Data Operations

  • Data Governance

  • Performance Monitoring

  • Pipeline Development

  • Infrastructure Design

  • Use Case Design

  • Continuous Improvement

  • Executive Dashboards & Reporting


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 a robust, continuous process of planning, executing, evaluating, and improving.

In this approach, the development of analytical deliverables is highly transparent to the stakeholders, and top-level engagement helps to dictate priorities and monitor progress. The success of agile is dependent on strong, cohesive teamwork and collaboration. The service should make a point to highlight and strengthen this area, ensuring that everyone is making fully informed decisions based on actionable data, information, and recommendations.

Benefits of this methodology:

- Stakeholder Engagement

- Transparency

- Early and Predictable Delivery Times

- Predictable Cost & Schedule

- Change Management Capabilities

- 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.

Collaborative data management focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.

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 can help provide a more efficient organization while simultaneously improving individual and group performance.


With a platform + service combination to Data Ops, organizations can better actively manage and improve the data ops life cycle and the specific criteria associated with it. It 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, ingest, 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 amongst teams

- Quick and effective responses to new requests

- Shorter response times to fixing bugs and defects

- Ability to predict scenarios for disaster avoidance

- Improved overall quality through statistical process control (SPC)

Specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics

According to Gartner, the average financial impact of poor data quality on organizations is $9.7 million per year. Poor data quality often stems from several areas.


Ever-growing, complex data environments typically mean that organizations are consistently behind the curve and are actively failing to unlock the full value of their data. This means that data cannot get the attention it deserves to make sure quality is up to standard.

In addition, 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. There is a significant amount of time spent searching for the "right" data than there is analyzing and making informed decisions.


Once that data is found by users, more time is dedicated to verifying that the data can be trusted, and 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 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. Taking a strong yet flexible 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

Digital experience monitoring (DEM), application discovery, tracing and diagnostics, and purpose-built artificial intelligence for IT operations. 

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 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 is decreasing with each added layer of complexity - whether that complexity is caused by people, process, or technology.


A Managed Analytics Service should significantly boost an organization's capacity to deal with outages, downtimes, and general performance issues by bringing transparency to an overly complex environment. It should be able to generate a clear overview of respective systems to help eliminate inefficiencies and identify efficiencies in their place. It should be able to rapidly improve response times, enhance predictability, and encourage proactiveness by identifying areas that may otherwise be overlooked.

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 provided by your Managed Analytics Service should be coupled with a streamlined approach to cut 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. Your chosen service should allow you to reallocate time to focus more on reviewing dashboards, reports, insights, and intelligent business decision-making.

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 a 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.

Benefits include:

- Increased productivity

- Greater Agility

- Improved quality

- Lower costs

- Decreased delivery times

Design, implement and maintain architectures 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 to 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 a focus on flexibility and adaptability to make your data infrastructure a long-term, lasting-impact solution.


Map, organize, and design models to fit detailed requirements

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 meet data strategy.


Your Managed Service should take a unique approach to use cases that 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 include:

- Higher application quality

- Quicker time to market

- Lower development and maintenance costs

- Improved data quality

- Better performance

- Documentation & knowledge transfer



Augment existing capabilities with state-of-the-art dashboard 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, cleanse, 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. 

Your Managed Analytics Service should help Data Engineers, Data Scientists, Business Users & C-level executives manage the entire data pipeline from source data to compelling reports and dashboards.

Ideally, the service should enable customers to gain never-before-seen insights from their data, which typically can only be harnessed from the use of Graph technology. The service should use Graph to allow clients to model, import, and analyze their data within an intuitive, graphically rich user interface.


The MAaaS should automatically identify relationships across datasets and deploy solutions that allow these relationships to be interacted with by clients, allowing all data consumers to drill deep into datasets to find impactful insights. 

Without an intuitive interface and strong data integration, clients won't see reduced development times. Insights will also take longer to accrue without out-of-the-box customizable reports and charts - make sure your managed service can accomodate this too!

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