[CONTENT]
From finance to the internet of things (IoT), the promise of today’s data-driven economy challenges organizations to incorporate diverse streams of data in unprecedented volumes, and create maximum value at the lowest cost. High-velocity analytics that cover in-the-moment and historical data in a platform like kdb creates intelligence that automates processes and decision-making, and builds significant business value.
kdb is an integrated data management and analytics platform for real-time decision-making in the cloud, on-premises, and at the edge. It powers a wide variety of use cases where large volumes of streaming data need to be integrated from a diverse set of inputs, analyzed in real-time, and enriched with historical context to drive critical, in-the-moment decisions. In the manufacturing and IoT sectors, the KX Data platform, powered by kdb is leveraged for component and systems management, as well as overall performance improvement. In this study, use cases included a range of customers in high-complexity manufacturing, prototype research and development, utility networks, and grid-based energy marketplaces. Each had a growing need to ingest, analyze and improve performance from their IoT-based data that exceeded the limits of existing architecture.
KX commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying kdb.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of kdb on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers with experience using kdb. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization.
Prior to using kdb, the interviewees’ organizations’ solutions to handle, manipulate, and analyze data were scattered across operations, enterprises, and IT systems. These legacy solutions were not well suited for IoT performance measurement and in-the-moment management. Further, the interviewees noted their organizations did not have the capability to compare real-time performance with stored data sets to have a broader statistical framework for action. As such, they had to invest significant human effort into data manipulation and analysis, and experienced longer product and performance improvement cycles, along with higher costs from early component failures and excessive component redundancies.
After the investment in kdb, the interviewees noted their organizations expended less analytical effort and had superior visibility into IoT performance, created faster and more effective iterative improvement cycles, and lowered costs with reduced incidents of component failure.
Consulting Team: Greg Phillips
KEY STATISTICS
-
Return on investment (ROI):
315%
-
Benefits PV:
$5.72M
-
Net present value (NPV):
$4.34M
-
Payback:
<6 months
KEY FINDINGS
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Reduced FTE burden for data manipulation and analysis worth more than $1.8 million over three years. Interviewees noted how the KX Data platform, powered by kdb significantly reduced the time required to ingest and manipulate the millions of data points edge device/IoT sensors generated into a form usable for data scientists. Interviewees noted this reduced the FTEs required for this task by more than 80%, freeing up resources to be utilized on other data projects.
- Faster product iteration with kdb, creating FTE cost savings totaling nearly $1.4 million. kdb provided improved analytical capabilities and speed, permitting the interviewees’ organizations to work through development, production, and network fixes as much as 85% faster compared to their previous capabilities.
- Improved product reliability enabled by kdb worth $1.6 million. In addition to faster product iteration, interviewees reported kdb capabilities knit together current stored data and network performance to support deeper statistical analysis. This resulted in tighter performance specifications as well as the capability to test and adhere to these in real-time, which significantly improved product performance and reliability, saving rework costs.
- Reduced cost of spare components inventory worth over $839,000 over three years. Improvements in product reliability driven by kdb created important downstream benefits. Interviewees determined component resilience over a wider range of circumstances, reducing costs for unneeded backup components and infrastructure.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- New vistas opened in data analysis. Interviewees related that the kdb’s ability to integrate streaming data and historical data sets created a deeper basis of information to conduct statistical analysis and relationships not possible in past solutions.
- Replaced specialized independent analytical assessments by third parties. Depending on the setting, systems performance testing through kdb replaced the cost of independent assessments, saving interviewees’ organizations thousands in external consulting and engineering costs.
- Replaced legacy equipment. Because kdb became the analytical focal point of IoT systems to monitor performance, interviewees replaced legacy systems for network outage management. One utility executive stated: “The cost of a standalone outage management system ran into the millions and was less effective in getting us back online. Our ability to run this with GIS [geographic information systems] visualization on the KX Data platform, powered by kdb all by itself far outweighs the cost of the management capabilities we invested in with kdb.”
Costs. Risk-adjusted PV costs include:
- kdb setup costs of less than $285,000. Establishing the KX Data platform, powered by kdb for data ingestion and analysis involved a core team of system architects, developers, and project managers on an initial 100-day sprint with a part-time basis across the team to set up ingestion of sensor/IoT data, analytical frameworks for streaming as well as stored data analysis, and create dashboarding and data visualization.
- License and core data maintenance costs of less than $1.1 million. The interviewees’ organizations deployed kdb to replace data manipulation and analytical computations they previously outsourced or conducted with labor input. The interviewees reported a net reduction in costs when comparing their organizations’ levels of expenditure and effort following the implementation of the KX Data platform, powered by kdb.
The decision-maker interviews and financial analysis found that a composite organization experiences benefits of $5.72 million over three years versus costs of $1.38 million, adding up to a net present value (NPV) of $4.34 million and an ROI of 315%.
Benefits (Three-Year)
Reduced FTEs for data manipulation and analysis Reduced FTEs for product iteration Improved product reliability Reduced cost of spare components inventoryTEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment kdb.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that kdb can have on an organization.
-
DUE DILIGENCE
Interviewed kdb stakeholders and Forrester analysts to gather data relative to kdb.
-
INTERVIEWS
Interviewed four representatives at organizations using kdb to obtain data about costs, benefits, and risks.
-
COMPOSITE ORGANIZATION
Designed a composite organization based on characteristics of the interviewees’ organizations.
-
FINANCIAL MODEL FRAMEWORK
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
-
CASE STUDY
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
DISCLOSURES
Readers should be aware of the following:
This study is commissioned by kdb and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in kdb.
kdb reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
kdb provided the customer names for the interviews but did not participate in the interviews.
Drivers leading to the kdb investment
Interviews
| Role | Industry | Region | Revenue |
|---|---|---|---|
| Innovation director | Utility | Europe | $1 billion |
| Vice president, system design and architecture | Energy marketplace | Europe | $800 million |
| Test engineering manager | High-complexity manufacturing | Europe | $500 million |
| R&D/data science engineer | High-performance prototype development | Europe | $250 million |
Key Challenges
Before investing in kdb, the interviewees’ organizations utilized different siloed software across operations, enterprise, and IT systems to ingest, present, and analyze their sensor-based data. As these organizations increasingly implemented IoT-based sensors, components, and infrastructure, the volume and velocity of this data grew exponentially. This created a series of costs as interviewees struggled to manipulate and analyze this data in a timely way, gain situational awareness, and take appropriate action.
The interviewees’ organizations struggled with common challenges in their prior environments, including:
-
Costs and additional human effort in data handling and analysis created with the implementation of IoT/edge sensors. The inability of existing solutions to keep up with data flows created lags in data timeliness and action. In short, more data didn’t make for a better product, and created additional costs in data manipulation and analysis. The innovation director at a utility organization noted: “The initial rationale for utilities’ move to smart meters was to latch onto process automation to reduce costs while improving data accuracy and billing. The reality is it also pushed us way up the analytics curve and created costs. It became too time-consuming and expensive for engineers to handle, let alone analyze all those data as it had been done.”
The vice president, system design and architecture for the energy marketplace contributed: “With liberalization and home solar, which also gets resold, utilities collectively represent an energy marketplace of many producers and consumers at all scales. It’s not just more data that must all be accounted for; the calculations themselves are more complex by a factor of 20. Before kdb, we had a team tasked with assembling and harmonizing the data hourly every operating day.”
- Limited real-time data value from the IoT readings, due to analytical load. Even with significant investment among their teams to manipulate and analyze the data streams, the resultant time lag challenged the interviewees’ organizations’ ability to create best-course steps or take decisive action. One interviewee noted: “It took a lot of work getting metric data from the raw test. The math had to be done externally in another system. It wasn’t timely and, ultimately, we didn’t know whether to trust what was a product issue or a sensor issue in performance.”
- The inability to act on sensor data or missed signals, which affected product performance and improvement cycles. This, in turn, created downstream costs in human rework effort and unnecessarily procuring redundant components. Interviewees lauded the signals kdb could provide in real-time. The test engineering manager related, “With kdb, our data sensors on the line can spot production, yield, and product reliability issues in real-time to take action.”
-
The inability to compare real-time and historical data sets, creating missed opportunities in product improvement. Test and performance data were all too often individualized events with existing platforms unable to draw on previous data sets to analyze in real-time against the generated streaming data. This left interviewees unable to delve into deeper statistical testing across a large array of results and acceptable performance bands that were too wide. This often led to unanticipated production errors, component outages, and longer iterative timelines to improve product longevity.
As the test engineering manager in high-complexity manufacturing related: “Previously, components would pass test parameters but could still fail with customers before their expected lifetime. Performance tolerances could not be tested at sufficient depth statistically without extending a costly effort to bring together historical test data to support this. The statistical testing available to us with the KX Data platform, powered by kdb is much richer, and the acceptable variances in production testing are much tighter now.”
The initial rationale for additional IoT, whether from edge device sensors in production, R&D, from sensors across a network, or grid (IoT), was to monitor and improve performance, reduce error, and save human effort. However, interviewees related that the exponential growth in data created a pressing need to ingest, manipulate, and analyze the data streams, which affected product performance and improvement, and caused them to look for a viable solution.
Investment Objectives
The interviewees’ organizations searched for a solution that could:
- Handle the ingestion of high-volume/high-velocity data across a variety of systems and devices.
- Reduce the effort in subsequent data manipulation and create reliable, real-time analysis from data streams.
- Add visibility to this data with analytical dashboards and signals.
- Unlock the value of streaming data further with real-time analytical capabilities and stored data sets.
- Improve the speed and quality of product iteration and fixes.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the four decision-makers that Forrester interviewed and is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization is a high-tech manufacturing organization with high-complexity and high-value manufacturing processes. It collects a multitude of component and systems performance data, which are intrinsic to supporting product specification, testing, and statistical analysis of the line production. The composite organization has a strong brand and global operations, wherein product performance is fundamental to its base of customers and its brand at large as a high-tech creator of value.
Deployment characteristics. The composite organization has specific critical production sites and has employed kdb on several manufacturing lines where testing and data collection efforts are cumbersome, slow, and incomplete. This requires significant time data manipulation and analysis. The organization is expanding the implementation of kdb to include a full array of its manufacturing activity, driving yield, profitability, and customer satisfaction, and minimize rework.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Reduced FTEs for data manipulation and analysis | $742,500 | $742,500 | $742,500 | $2,227,500 | $1,846,488 |
| Btr | Reduced FTEs for product iteration | $536,399 | $569,924 | $569,924 | $1,676,247 | $1,386,840 |
| Ctr | Improved product reliability | $594,000 | $700,000 | $700,000 | $1,994,000 | $1,644,433 |
| Dtr | Reduced cost of spare components inventory | $337,500 | $337,500 | $337,500 | $1,012,500 | $839,313 |
| Total benefits (risk-adjusted) | $2,210,399 | $2,349,924 | $2,349,924 | $6,910,247 | $5,717,074 |
Reduced FTEs For data manipulation and Analysis
Evidence and data. The interviewees noted that the KX Data platform, powered by kdb enabled their organizations to ingest and analyze data streams at a faster rate, and saved a critical step in data manipulation that took place before these high volume/high-velocity data streams could be analyzed.
- Before investing in the KX Data platform, powered by kdb, the interviewees’ organizations faced limitations in the volume and speed of data handling. The organizations described the significant effort required by their engineering and analytical teams to manipulate these data into actionable information, which routinely created significant lags from sensor reading to presentation of information. These shortcomings caused organizations to act too slowly, which often created other downstream costs.
- After deploying the KX Data platform, powered by kdb, organizations freed themselves from the scale, speed, and cost limitations of their high-volume/high-velocity sensor data and acted on this data in a timely way. This created further benefits in faster product iteration, improved product resiliency, and fewer redundant parts required, all in a virtuous cycle. The test engineering manager at a high-complexity manufacturing organization stated: “Before the kdb analysis, sorting through our test results was very manual. Engineers used to jump between two or three tools and would take hours to pull together the analysis. Speeding this, alone, creates higher production for us.”
- The R&D/data science engineer at a high-performance prototype development organization added: “More than 80% of our time was taken to get the data into a form that permitted good data science from the test result. KX Data Platform, powered by kdb means our engineers can spend time on science and not data transformation.”
- The innovation director at a utility organization noted: “kdb also saves significant time for our analysts by calculating reliability statistics digitally and automatically. Previously, these records were created from data entry, manipulation, and analysis to create the document record, all of which could also be subject to human error.”
Modeling and assumptions. For the composite organization, Forrester assumes the following:
- One data engineer can handle the data manipulation and analytical tasks that six had been working on prior to the KX Data platform, powered by kdb reflecting the labor savings described in the interviews.
- The average fully burdened annual salary for an engineer is $165,000.
Risks. The reduction in the FTE requirements of data manipulation and analysis will vary with the volume and velocity of an organization’s current and anticipated IoT networks. Other organizations may also have fewer intensive data handling scenarios than the FTE described in the model.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.8 million.
Reduced FTEs For Data Manipulation and Analysis
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| A1 | FTE engineer resources for data manipulation and analysis prior to kdb implementation | Composite | 6 | 6 | 6 |
| A2 | FTE engineer resources for data manipulation and analysis after kdb implementation | Composite | 1 | 1 | 1 |
| A3 | FTE engineer savings | A1-A2 | 5 | 5 | 5 |
| A4 | Engineer fully burdened salary (annual) | Composite | $165,000 | $165,000 | $165,000 |
| At | Reduced FTEs for engineering analysis | A3*A4 | $825,000 | $825,000 | $825,000 |
| Risk adjustment | ↓10% | ||||
| Atr | Reduced FTEs for engineering analysis (risk-adjusted) | $742,500 | $742,500 | $742,500 | |
| Three-year : $2,227,500 | Three-year present value: $1,846,488 | ||||
Reduced FTEs For Product Iteration
Evidence and data. The interviewees shared that the speed of the KX Data platform, powered by kdb for monitoring and acting on analytics translated into cost savings from reduced time for product iteration and development.
- Prior to the capabilities of kdb, the interviewees’ organizations spent more time in their workflow to identify component or systems issues and required improvements.
- After investing in the KX Data platform, powered by kdb, interviewees reported time savings from 80% to 85% in working through issues and actioning the needed resolutions.
- The testing engineer manager at a high-complexity manufacturing organization stated: “In our testing, kdb presents in real-time which components are more prone to deviation, affecting [product] performance and reliability. This used to require significant time and effort to spot. We now get to the issue right away.”
- The R&D/data science engineer at a high-performance prototype development organization related, “Because kdb draws from sensor-based test iterations anywhere, it helps drive in-the-moment performance of our prototypes not just in the lab but in the real world, allowing us to iterate more often and more effectively.”
- The innovation director at a utility organization stated: “With kdb, we can probe the grid in real-time to pinpoint the fault location and component. Previously, we could only receive outage messages in pockets, so we would have to send more crews into a broad area and follow the lines, then once determined, send a follow-up truck with the right equipment.”
- The vice president, system design and architecture at an energy marketplace organization stated: “kdb eliminates the several iterative steps in market settlement with a dashboard that captures, analyzes, and completes the data and billables of thousands of energy producers and consumers, and can differentiate what is normal electromagnetic loss versus missing data in minutes versus the hours it took previously.”
Modeling and assumptions. For the composite organization, Forrester assumes the following:
- Ten data engineers spend 60% of their time on product iteration
- The composite experiences time savings of 80% to 85% in faster issue identification and resolution
- There is a 75% productivity recapture rate.
- The hourly fully burdened salary for a production engineer is $79.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV of $1.4 million.
Reduced FTEs For Product Iteration
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| B1 | Annual product iteration hours prior to kdb (60% of FTE) for 10 production engineers | 10*2087*60% | 12,522 | 12,522 | 12,522 |
| B2 | kdb speeding product iteration/completion factor | Interviews | 80% | 85% | 85% |
| B3 | Engineer hours saved for product iteration | B1*B2 | 10,018 | 10,644 | 10,644 |
| B4 | Productivity recapture rate | Composite | 75% | 75% | 75% |
| B5 | Production engineer fully burdened salary (hourly) | Composite | $79 | $79 | $79 |
| Bt | Cost savings in reduced FTE for product iteration | B3*B4 | $595,999 | $633,249 | $633,249 |
| Risk adjustment | ↓10% | ||||
| Btr | Cost savings in reduced FTE for product iteration (risk-adjusted) | $536,399 | $569,924 | $569,924 | |
| Three-year total: $2,227,500 | Three-year present value: $1,846,488 | ||||
Improved Product Reliability
Evidence and data. The interviewees shared that the capabilities of the KX Data platform, powered by kdb allowed them to identify and address component and systems issues to drive improved product reliability.
- Prior to kdb, the interviewees’ organizations could not complete deeper analysis, which led to their facing rework costs due to lower-than-expected product life, resiliency, or service levels.
- After investing in kdb, the cost of rework required to meet expected product lifetime performance was reduced by as much as 78%.
- The innovation director at a utility organization stated: “kdb allows us the benefits of having all these edge devices collecting information. The ‘seeing’ of these nodes on the network not only speeds how we recover faster from outage but also in how we improve reliability by testing growth tolerances with how additional load affects the lifespan of specific equipment nodes.”
- The testing engineer manager at a high-complexity manufacturing organization added: “We can react even if not in front of the dashboard and there’s a test out of acceptable range. Because we are now able to iterate on the issue right away, we save money in reduced waste and rework.”
- The R&D/data science engineer at a high-performance prototype development organization related: “Our previous means was fine for running a few channels of data, but with the explosion of data we now collect from sensors across every system, the math was a minute-and-a-half behind. That’s too late to intervene and prevent failure. Now, we take advantage of end-to-end monitoring in real-time on kdb to improve performance.”
Modeling and assumptions. For the composite organization, Forrester assumes the following:
- The composite organization has an estimated $1 million rework annually on $1 billion in revenue to meet promised SLAs (0.1% cost basis).
- The component failure rate before kdb is 4.5%. After kdb, it’s 1.5%, dropping to 1.0% in Year 2 and Year 3.
- The reduction in rework based on executive interviews ranges from 67% to 78% over three years.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV of $1.6 million.
Improved Product Reliability
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| C1 | Component failure rate prior to kdb | Interviews | 4.5% | 4.5% | 4.5% |
| C2 | Component failure rate after kdb | Interviews | 1.5% | 1.0% | 1.0% |
| C3 | Change in less frequent rework | (C2-C1)/C1 | 67% | 78% | 78% |
| C4 | Cost of rework prior to kdb | Composite | $1,000,000 | $1,000,000 | $1,000,000 |
| C5 | Cost of rework after kdb | C4*(1-C3) | $340,000 | $222,222 | $222,222 |
| Ct | Improved product reliability | C4-C5 | $660,000 | $777,778 | $777,778 |
| Risk adjustment | ↓10% | ||||
| Ctr | Improved product reliability (risk-adjusted) | $594,000 | $700,000 | $700,000 | |
| Three-year total: $1,994,000 | Three-year present value: $1,644,433 | ||||
Reduced Cost Of Spare Components Inventory
Evidence and data. Interviewees noted the visibility of nodal data across systems facilitated allowed them to reduce the redundancy of spare parts.
- kdb allowed the interviewees’ organizations to see component variances under different operating conditions and get to root failure issues more quickly and definitively. As such, the organizations stress-tested systems before failure, reducing parts needs and better determining which resilient parts were most likely to fail. This allowed them to concentrate on these and not stock for full system redundancy. This reduced the costs for spare components.
- As testing engineer manager at a high-complexity manufacturing organization noted, “Because we are able to narrow the critical components prone to variation, we have saved costs by cutting down on the number of other potential replacement parts we have to procure.”
- The innovation director at a utility organization stated, “With lower failure rates in the grid following kdb, now we can stock fewer parts and purchase less major infrastructure.”
Modeling and assumptions. For the composite organization, Forrester assumes the following:
- The high-value manufacturing composite organization saves 7.5% of its $20 million annual redundant components inventory.
- The standard carrying-cost factor to this inventory is 25% of this value as a net cost reduction.
Risks. Other organizations may have differences in their redundant component inventories or annual costs.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV of $839,000.
Reduced Cost Of Spare Components Inventory
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| D1 | Value of noncritical components in inventory prior to kdb identifying critical parts | Composite | $1,500,000 | $1,500,000 | $1,500,000 |
| D2 | Carry-cost factor to components inventory | Interviews | 25% | 25% | 25% |
| Dt | Reduced cost of spare components inventory | D1*D2 | $375,000 | $375,000 | $375,000 |
| Risk adjustment | ↓10% | ||||
| Dtr | Save costs of spare components inventory (risk-adjusted) | $337,500 | $337,500 | $337,500 | |
| Three-year total: $1,012,500 | Three-year present value: $839,313 | ||||
Unquantified Benefits
Additional benefits that customers experienced, but were not able to quantify include:
- New vistas opened in data analysis. kdb gave the interviewees’ organizations a deeper look into both streaming and historical data sets. This allowed the organizations to conduct statistical analysis and relationships not possible in past solutions.
- Replaced legacy systems. In certain applications, the KX Data platform, powered by kdb created capabilities more thorough than existing systems and permitted their replacement. For example, the KX Data platform, powered by kdb, when also linked with a GIS framework, provided more detailed information on equipment functionality than legacy systems, making it redundant. The innovation director at a utility organization related: “The cost of a standalone outage management system ran into the millions and far outweighed the cost of our management capabilities on a KX Data platform, powered by kdb. It is something we are able to fully replace because of this investment in kdb.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement kdb and later realize additional uses and business opportunities, including:
- Growing network and components capabilities with effective, rational insights that save on consulting costs. The innovation director at a utility organization added: “Previously, I would have to commission assessments costing many thousands each time I wanted to consider changes or the impact of a big customer on the grid. I can test upfront the impact of any change, adding load, a generator, adding battery storage to understand the/any/future impact from reliability to investment, and make informed decisions.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | kdb sprint team setup cost | $284,625 | $0 | $0 | $0 | $284,625 | $284,625 |
| Ftr | Kdb license and data core maintenance | $0 | $440,000 | $440,000 | $440,000 | $1,320,000 | $1,094,215 |
| Total costs (risk-adjusted | $284,625 | $440,000 | $440,000 | $440,000 | $1,604,625 | $1,378,840 |
KDB Sprint Team Setup Cost
Evidence and data. Interviewees noted the costs to set up and integrate disparate data ingestion into the KX Data platform, powered by kdb across system components and sensors, as well as tap into stored data for sub-millisecond analysis and create data visualization and dashboarding.
In a standard application of kdb, a sprint team would be designated to map out these data, integration, and analytical tasks. These would be specific given the organizational context, but routinely this would include experts in solution architecture, software development, business analysis, and project management in a targeted engagement for the particular setting. Interviewees described a three- to six-month timeframe for implementation.
Modeling and assumptions. For the composite organization, Forrester assumes the platform setup is accomplished in a 100-day sprint with the team handling its roles in some portion of 25% to 50% engagement over the duration of the sprint from design to delivery.
Risks. The composite organization falls towards the middle of the experience related in most setups; however, different organizations could have different timeframes and intensities for the design and setup of the platform based on IoT based-sensors and networks.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of less than $285,000.
kdb Sprint Team Setup Cost
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Approximate 4.5 months set-up sprint | Composite | $0 | |||
| E2 | Solution architect fully-burdened salary over 35 days (35% of time) | TEI standard | $61,250 | |||
| E3 | Project manager fully-burdened salary over 35 days (35% of time) | TEI standard | $52,500 | |||
| E4 | Business analyst fully-burdened salary over 50 days (50% of time) | TEI standard | $60,000 | |||
| E5 | Two software developers fully-burdened salary over 50 days (25% of time) | TEI standard | $55,000 | |||
| E6 | Junior developer fully-burdened salary over 25 days (25% of time) | TEI standard | $18,750 | |||
| Et | kdb sprint team setup cost | E2+E3+E4+E5+E6 | $247,500 | $0 | $0 | $0 |
| Risk adjustment | ↑15% | |||||
| Etr | kdb sprint team setup cost (risk-adjusted) | $284,625 | $0 | $0 | $0 | |
| Three-year total: $284,625 | Three-year present value: $284,625 | |||||
Kdb License and Data Core Maintentance
Evidence and data. For the composite organization, the annual cost of the KX Data platform, powered by kdb is estimated to total $300,000 annually. Ongoing work to maintain the core databases, develop and rung scripts to handle the velocity of data for ready comparison to stored data, as well as handle the changes and evolution in core data sets is estimated to cost $100,000 annually.
Modeling and assumptions. For the composite organization, Forrester assumes the following:
- The composite organization engages KX for these core maintenance tasks, but these could also be met with internal IT resources dedicated to this element.
- The license and core data maintenance costs are constant across the three-year economic model.
Risks. Depending on the application, some organizations may have different degrees of core data maintenance updates and needs.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV of less than $1.1 million.
kdb License and Data Core Maintenance
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | kdb license cost | Composite | 300,000 | 300,000 | 300,000 | |
| F2 | Data core maintenance | Composite | 100,000 | 100,000 | 100,000 | |
| Ft | kdb license and data core maintenance | F1+F2 | $0 | $400,000 | $400,000 | $400,000 |
| Risk adjustment | ↑10% | |||||
| Ftr | kdb license and data core maintenance (risk-adjusted) | $0 | $440,000 | $440,000 | $440,000 | |
| Three-year total: $1,320,000 | Three-year present value: $1,094,215 | |||||
-
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each benefit and cost section.
Cash Flow Chart
Total costs Total benefits Cumulative net benefitsCASH FLOW ANALYSIS (RISK-ADJUSTED ESTIMATES)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($284,625) | ($440,000) | ($440,000) | ($440,000) | ($1,604,625) | ($1,378,840) |
| Total benefits | $0 | $2,210,399 | $2,349,924 | $2,349,924 | $6,910,247 | $5,717,074 |
| Net benefits | ($284,625) | $1,770,399 | $1,909,924 | $1,909,924 | $5,305,622 | $4,338,234 |
| ROI | 315% | |||||
| Payback period (months) | <6 |
[CONTENT]
Appendix A: Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
-
Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
-
Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
-
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
-
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
APPENDIX B: ENDNOTES
1Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s
technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
PDF