The field of Marketing provides some transformational parallels for the HR professional looking for a change in perspective.  A poorly marketed product suffers from internal and external obstacles:

1. lack of sales = engagement (internal) and recruiting (external)
2. poor customer retention =  satisfaction (internal) and turnover (external)
3. weak brand recognition = culture (internal) and communications (both internal and external)

Although things are changing for the better in many organizations, this probably sounds familiar to many HR professionals.

Market Segmentation in HR

Geographic segmentation:

• Metro area
• Region
• Country

Customer segmentation:

• Executive, Managers, Operations
• Language, ethnicity
• Gender
• Salary ranges
• Tenure
• Working time
• Contract (hourly, salary, etc.)
• and more…

4 P’s of Marketing - Product, Price, Promotion, Place

Product - What do we offer relative to other employers (what are we selling? what specific services, products?)

Price - Are we competitive with industry and geographic averages?  what does it ”cost” an employee to work here or what do they gain? Recognition? Commute time?  Family time? Lifestyle?  Flexibility?

Promotion – How do we get our message out internally and externally?  web portal, social media, email, phone, events, other?

Place - How do we reach the target audience segments (internal and external)?

This is not a top down approach to HR.  To get a “seat at the table”, “join the C-Suite” or whatever you want to label it, this needs to be bottom up or a grass roots Human Resources strategy.  While top down  can be important for setting and communicating strategic targets, bottom up is what gets real results.

So, if you find yourself lamenting the lack of interest from the executive suite to accomplish these things, it is time to start marketing HR’s value in your organization.  HR Analytics is a critical part of that strategy.  Marketing professionals were quick to capitalize on big data analytics via the web years ago. HR should be looking at that evolution as a roadmap.

Also see: The Future of HR Master Data Management

However, HR is unique and always has its own set of needs that is often wildly different from traditional BI heavy functional areas such as point-of-sale systems, shipping and receiving, and finance.  I’ll start by outlining some of those differences as they relate to BI:

1. The volume of data in a typical HRIS instance, even for large organizations, is not more than 1MB per employee per year even including database space heavy details like paychecks and time cards (for 10,000 employees, 10 years of data, that’s still only 100GB and half that without payroll and time details). 
2. HR is adopting SaaS quickly in many cases due to the cost efficient models and the highly specialized, easy to use  functionality (think Taleo, Kenexa, Cornerstone OnDemand, etc).
3. Social data, some would argue, is more important to HR than to other functions (especially in Talent Management).
4. To gain buy-in from organizations, HR needs to be able to release data and reporting to managers and even employees in some cases without compromising data security (via Manager self-service and Employee self-service).

Summarizing these themes, the areas that are important to many HR departments in a BI solution right now are:

• Cost efficiency
• Ease of Integration (with Saas and internal applications alike)
• Usability (to deliver to Managers and Employees with minimal training)

Having seen a demo of Hana I can say I am not yet convinced it achieves these for HR specifically.

First, implementing an instance of Hana starts about $US 500,000 (see SAP blog) including licenses, hardware, and setup.  Oracle’s Exadata is in the same ballpark, and as SAP claims above, even beyond.  I think it is safe to assume only the largest firms will be willing to spend $US 500k on hardware for HR alone.  As a result, HR is not going to be leading the purchasing decision.  Therefore how influential can HR be in its design and deployment?  It is important to note this cost includes the box only with little content and little out of the box integration (except internal to SAP modules).   So, plan on spending some money for implementation and then for consulting and/or full time developers to build reports.  It is unclear whether Business Objects licenses have to be bought in addition.  The total likely expense for Hana HR report development, the Hana server and implementation seems to easily reach $US 1 million or higher to have a server and full reporting suite developed for global HR.

Integration has always been heavy work for SAP customers.  Even specialized integration solutions like SAP PI cost a lot of time and money to maintain outside the normal licensing expense.  As I understand it, external non-SAP data still has to be replicated into the native Hana DB, it cannot be pulled real time from external source systems for in-memory reporting (someone correct me on this if I’m wrong- here’s the manual).  That means Taleo, Cornerstone, etc. data has to be batched into the database just as it did with traditional ETL tools.  SAP also has had a long standing strategy of making up its own hybrid integration standards (Business HTML is a “great” example) rather than opening up to industry and community standards.  So, it remains to be seen how well Hana can easily integrate with SaaS providers to acquire all the data HR needs to deliver a sometimes elusive value proposition.

Lastly, usability is definitely an improvement in Hana.  The charts are highly interactive and lend themselves to easier manipulation by users on the fly due to its in-memory architecture.  Having said that, I have also seen some other notable in-memory SaaS reporting applications such as Birst and QlikView which can deliver similar performance and look and feel for a very small fraction of the cost of Exadata and Hana.  Based on first impressions, SAP Hana is not delivering anything revolutionary for HR end users that would warrant the much larger expense for HR alone.

However, Hana is catching on quickly in the marketplace and that is likely because it is a clear upgrade for BW / BI and it is a testament to SAP’s very effective Sales and Marketing engine.  For those firms currently on multiple SAP modules and without plans to change systems anytime soon Hana is likely a no brainer investment decision.  At the same time I have not seen anything yet that is going to allow HR to be transformational in its delivery without partners helping to fill the content and integration voids.  That has always been critical for the SAP ecosystem.  There is no doubt partners will come through in this market, but expect it to take some time to mature.  Until then companies should plan on spending money to hire good consultants who can help build out Hana content.

1)  Process globalization

2)  Software fragmentation (SaaS)

3)  Data Proliferation / Increasing Compliance Risk

The HR data warehouse is the long term solution to manage cost and risk while continually adding value to support HR as a strategic business partner.  This approach answers many of the questions currently plaguing HR and IT landscapes today and well into the future.

How can I achieve fast ROI across my global organization with reporting and analytics?

Many organizations made the business case for multi-million ERP implementations to simply gain consolidated global headcount reporting.  Unfortunately ROI was much harder to achieve than anticipated in many cases and now a simple global headcount report alone is not enough to serve the strategic business needs.

Consolidate global systems for one view of your data across geographies, languages, and currencies.

With a global HR Data warehouse, you can achieve the same reporting and analytics benefits for a fraction of the cost of reimplementation of landscapes which continually shift over time as it is.  Implementation time is a microscopic fraction of that of ERP systems and because there are no internal staff or IT infrastructure requirements, organizations can achieve ROI immediately.

How can I avoid losing reporting functionality across HR systems?

The fragmentation and specialization plus best of breed SaaS application adoption has organizations losing reporting capabilities across functions of the Human Resources and Payroll domains.

Consolidate data from fragmented “best-of-breed” enterprise software throughout your organization.

As SaaS options in the HR technology landscape come and go through growth and acquisitions (such as Taleo by Oracle, SuccessFactors by SAP) organizations need a consolidated platform for reporting and analytics.  Although many of these transactional systems offer analytics and reporting functionality, without data all in one centralized, global application organizations cannot slice and dice strategic data across all Human Resources functions.  In addition to providing self service reporting and analytics across HR and Payroll functions, this also enables centralized security and allows data retention compliance policies to be monitored and enforced in one place.

How can I decommission old legacy systems and retain data history for compliance?

As technology changes over time and organizations look to constantly better serve their strategic objectives, systems landscapes also naturally shift.  HRIS systems typically have a lifespan of 7-10 years and that could be shortening with lower TCO (Total Cost of Ownership) and lower exit barriers which SaaS applications promise to deliver on.

Strategic HR Data Management across time through changing HR and IT landscapes.

As organizations migrate from one HR system to the next, data history is almost always left in the outgoing legacy systems for audit and compliance purposes.  Transactional HR systems require mapping to configuration which does not exist historically.  The Fuse Analytics data warehouse leverages system agnostic HR-XML based data models which can be mapped to any HR system while also storing native table values side by side.  This permits a consistent and continuous  10+ year view of data from multiple systems even while organizational configuration and structures shift over time.

HR data history is often required for a minimum of 7 years for compliance purposes which means keeping “dead” legacy systems up and running.  A data warehouse allows IT the flexibility to quickly trim HR systems from the landscape to cut costs.

Build or Buy?

Some of the benefits above might be achieved by building an in-house HR data warehouse.  However, the Fuse Analytics Saas HR platform offers many advantages over the traditional data warehouse implementation.

Who is it for?

A company who has a fragmented network of net payroll providers and needs to globalize reporting without re-engineering and re-negotiating the entire Payroll vendor network worldwide.  

HR is moving toward a global Service Center model.  Payroll is outsourced in some countries to various vendors and run in-house for others.  However, some local payroll providers provide a more cost effective service than could be achieved by implementation of a new global platform for payroll, but the organization wants one single point of access for all employees for all HR and Payroll related functions.

Achieving global payroll reporting out of a single platform can be costly and sometimes ineffective.  Read the whitepaper from our blog outlining the challenges with global payroll platforms: “Why Global Payroll Typically…Isn’t“.

Small country payroll solution:  provide a cost effective reporting platform for organizations with headcount spread thinly across countries (less than 100-200 active employees). Consolidated employee pay portal: store payroll detail and PDF stubs for employee access in a single portal view regardless of net payroll platform. Global Payroll reporting and analytics: provides a baseline data model for analytics on payroll data globally with single currency reporting capability.

A company looking to clean up their HRIS landscape and decommission legacy ERP or ASP systems locally or globally by archiving for compliance and audit needs.

IT wants to turn off old systems and databases to streamline the IT landscape and cut costs.  They suggest archiving the old legacy system to tape.  However, HR wants real time access to historical data in the event of audits for compliance, not an SLA with the IT department requiring days or weeks turnaround for data retrieval.

Live archive: Never lose access to your archived data and documents for compliance audit.  Retain data online for retrieval and reporting any time you need it and as far back in time as you need it. Fast browsing and reporting: Easy web-based interface emulates typical HR transactional systems allowing HR users to intuitively search and browse employee record history. Compliance audit tools: Ensure data retention policies are enforced across your Global HR and employee data and documents.

A company in need of a global HR analytics data model and data warehouse Platform-as-a-Service.

A global bank wants to provide HR with Analytics across the global organization from a single global PeopleSoft system without spending millions on an Analytics platform and the required infrastructure.

Intuitive analytics: browser-based platform makes exploring your HR data and analytics as easy as surfing the web. Fast ROI: Leverage out of the box HR best practices analytics using internal company data and external competitive data from around the web. Global HR reporting and analytics: provides a comprehensive baseline HR data model for analytics on data globally with multi-language reporting capability for your global HR organization.

What if we have already invested in a corporate business intelligence platform (SAP BI, Business Objects, Oracle OBIEE)?

No problem.  On-premise installations of the Fuse Analytics database can be integrated with these products as well.  None of these platforms are able to provide a usable baseline HR global data model out of the box. Each provides a customizable catch-all reporting platform which often results in redundant development and very few re-usable components.  In addition, the biggest user complaint of these ERP-style packages is lack of usability which is a hindrance to adoption by the enterprise wide group.  In HR, usability is critical.

Fuse can deliver 80% of HR’s needed analytic reporting for many organizations at a fraction of the cost of other platforms and it does not require a full time staff of specialists to maintain.  It is not just a flexible reporting platform but a full HR Analytics application that provides a global data structure, communications, compliance tools, and intuitive web-based interface not found in many of the legacy ERP BI platforms.

Some of the key strategic HR features Fuse Analytics offers which are not implemented or not possible in typical data warehouse and business intelligence platforms are:

• Protection of PII (Personally Identifiable Information) and HR-centric security roles.
• HR employee and company documents (tax forms, garnishments, job specifications, etc.) linked to employee and company data records in a single portal.
• Legal limits on data and document retention policies can be easily enforced without compromising data and reporting.
• Leverages not only internal HRIS data, but adds external data (salary surveys, Maps APIs, benchmarking, etc.) from many sources to deliver HR-based analytics functionality which would normally be cost-prohibitive to develop in-house for HR.

Take the Tour…

I will cover a brief introduction to the terminology of Business Intelligence to clarify the topic covered herein.  A metric, or a fact is a specific data point:

-  turnover % (heads terminated/total [active] heads)

-  revenue per person (total revenue $/total [active] heads)

-  ratio of HR administrators to Employees (total [active] HR employee heads: total [active] heads)

Business Intelligence facts are typically not open to interpretation, but instead fixed and charted over time for different organizational segments.  They can be absolute counts, percentages, ratios, or any other mathematical measure.

Analytics  refer to the mining,  measurement and reporting of facts or metrics (often presented visually) over time as they relate to one another.  Although this realm grows extremely complex in areas such as quantitative risk analysis (often used in financial services) for the purposes of Human Resources we only need to get familiar with some basic concepts:

positive correlation:  in a sample set of data points what % probability will one metric relate to another or move in parallel with another metric forming a ‘//’.

negative or inverse correlation:  in a sample set of data points what % probability will one metric relate to another’s polar opposite, meaning they move in exact opposite directions forming an ‘X’.

For a more detailed intro to HR Analytics read the blog post:  Human Resources Analytics Primer.

Predictive Modeling Errors

I happen to buy mostly business books from Amazon.com most often and for that reason I am presented with quirky recommendations such as “Who Moved My Cheese” courtesy of Amazon’s effective predictive analytics engine.  While some of the recommendations are interesting, others miss the mark completely.  The most frightening result of my shopping habits, as it applies to the Human Resources domain, is that I’ve now found that it will only recommend a very narrow genre of books.  I’ve lost all random and creative associations that used to lead me to surprising new genres and authors.  It has become very Stepford in its associations because the data it leverages is incomplete.

This same Stepford-effect is a risk of predictive analytics in Human Resources.  If I happen to hire a few great performing 20-something-year-old men then that pattern becomes part of the predictive criteria and in turn encourages me to hire more young males.  This is exaggerated to make a point, but it is the same effect witnessed as a result of failing risk models of the subprime mortgage induced market crash (NY Times) and should not be underestimated or dismissed as unrelated.  The full set of data was not possible to obtain or was not considered.  Quantifying and predicting human behavior requires far more data than is possible to obtain with today’s Human Resources software and technology.  This affects many areas of Human Resources but specifically it impacts the more behaviorally nuanced areas such as Talent Analytics: Performance, Recruiting, and Training.

Predictive Analytics are what they “eat” and in Human Resources that amounts to very limited and tightly controlled data today.

Technology Outpacing Operations

Technology also quickly outpaces corporations’ ability to comprehend and effectively train on its usage.  Predictive Analytics reporting especially, due to its complex composition, is not something the average manager is trained to reverse engineer (without great effort) in order to challenge the underlying assumptions.  Therefore, they will typically either be forced to choose to ignore or accept the information at face value.  This is akin to using autopilot without being able to manually fly a plane as well.  As referenced earlier, this is also what afflicted financial services companies who fell victim to the subprime mortgage fallout.  Many people at the top of these firms had very little insight into what sort of details were built into the technical risk models to manage the trading of these instruments.

HR has historically been a far less quantitative business function than other areas such as Finance or Technology.  The lack of insight into these models can erode competitive capabilities very quickly and by the time you begin to lose key resources it may be too late to address the operational causes.

Dehumanizing Human Resources

People need shortcuts in the modern day world to cope with the vast amount of data we synthesize every second of every day.  Visual reporting has come a long way and when it is supported by details for further evaluation it is a powerful tool.  However, there is a sinister side to visual reporting and data mining that sometimes (intentionally or unintentionally) can influence business decisions where they should not.

When I was consulting for a client several years ago, there was a request for our team to produce a report to show a “day in the life” of users by name.  We went to great lengths to produce a nicely formatted, visual report allowing managers to see what activities were being completed by which users and when.  We were proud of this user-friendly development until we learned later that it was being used to help justify layoffs.  The tool was neither written to account for many non-system related tasks nor did it by any measure show how productive an employee was in his or her role.  The reality was that Management wanted to dehumanize difficult decisions.  They wanted the job of laying people off to be made less personal and more quantifiable whether it was accurate for this specific purpose or not.

Reporting software vendors are all too willing to oblige and managers all too willing to use the reporting at face value without proper due diligence.  Predictive Analytics in particular in Human Resources is inevitably turned into a “who-to-hire and who-to-fire” tool, a major part of the HR function’s responsibilities and it should not be blindly automated.  While the function of Finance is often to look quantitatively at decision points, HR should offer a counterbalance to this.

Making a Positive Impact

The objective is not to argue that Predictive Analytics cannot be used effectively or responsibly in HR with positive impacts.  There are many beneficial ways Predictive Analytics can be used in HR (among countless others):

• Determine what new Benefit plans are employees likely to leverage
• Estimate spikes in Absences or Overtime at certain points during the year
• Estimate correlated voluntary turnover as a function of involuntary workforce reductions (collateral turnover)

There is a great book that should be mandatory reading for Analytics professionals in general:  Thinking, Fast and Slow by Daniel Kahneman.  Although its focus is on the mind and its shortcomings in critical thinking, it also covers a lot of the risks alluded to above in data mining and analytics.

Major tectonic shifts are taking place at the intersection of HR and Enterprise Applications.  Human Resources and IT professionals need to start thinking about long term landscape architecture and master data management strategies.  Organizations are finding themselves being pulled at varying speeds into a new frontier of HR and technology and for many forward looking companies the challenges below are already affecting day to day operations.

Some of the significant trends covered below are creating the need for new applications, new strategies, and new organizational capabilities resulting in the accelerating transformation of the HR function into a true strategic business partner.

Functional Specialization

HR functions are fragmenting to better address organizational needs

 Technical Specialization

Organizations are moving from a "hub & spoke" landscape model to a "application matrix" landscape

Globalization

HR continues to develop from a local function into a global function

The subsequent 3 parts of this series will cover each of those forces above and delve into the impacts each will have on the future operating model for HR, IT, and on HR software vendors.

Metrics

A metric, or a fact is a specific data point:

-  turnover % (heads terminated/total [active] heads)

-  revenue per person (total $/total heads)

-  ratio of HR administrators to Employees (total HR employee heads: total heads)

The above represents the number of employees who voluntarily left the organization during a the time period selected.

Facts are typically not open to interpretation, but instead are fixed measurements and are charted over time for different segments of an organization.  They can be absolute counts, percentages, ratios, or any other mathematical measure.

Metrics or facts are also often synonymous with “KPIs“ ( Key Performance Indicators ).

Analytics

Analytics  generally refer to the measurement and reporting of metrics (often presented visually) over time as they relate to one another.  Although this realm grows extremely complex in areas such as quantitative risk analysis, often used in financial services, for the purposes of Human Resources we only need to get familiar with some basic concepts:

correlation  (can be a Positive or Negative relationship): in a sample set of data with 2 groups, this is the strength with which the 2 groups relate to one another on some metric across time. Graphically, a positive relationship is demonstrated by 2 lines moving in parallel across time – as one metric goes up the other goes up (as seen below) and vice versa.

The above displays the number of voluntary leavers graphed alongside the involuntary terminations for a time period.

inverse correlation (also called a Negative Correlation): in a sample set of data with 2 groups, this is the strength with which the two metrics are opposed to one another, moving in opposite directions. Graphically this relationship is demonstrated by 2 lines moving in opposite directions across time – as one metric goes up the other goes down, like an “X”.

time series:  a block of data points for a defined time period (for a specific metric or fact) which, in the Human Resources domain, is typically represented by a monthly, quarterly, 6-month, or annual period.   This permits an analytics engine to compare a metric over different time periods to check for correlations or inverse correlations.  It also enables software to perform predictive analytics on future metrics based on actual historical data.  See the time series graphic below.

Predictive Analytics

regression modeling: A regression model simulates probable outcomes of a particular metric based on other correlated metrics or variables.  For example, I may want to predict (assuming there is a correlation) probable turnover in the next 6 months based on an increase in the absence rate over the past 6 months.

In the graph above, the time period is subdivided into 3 periods to compare each period to the others to check for a relationship between the two metrics (1-versus-2, 1-versus-3, and 2-versus-3).  The faint yellow shading highlights a seemingly strong positive relationship between absence rate increase in the 6 months of segment number ’2′ and the voluntary leaver rate in segment number ’3′.  Meaning, an increase in absences may be pointing to future increased employee departures over the next 6 months.

These can be further broken out into the different time periods described above (3, 6, 12 month segments) for a more detailed analysis of positive or negative relationships (correlations).  These confirmed relationships are then used to estimate probable future outcomes based on data currently available for regression modeling.  Regression models can get far more complicated where data is available, but in Human Resources companies are mostly limited to the typical scope of HRIS data which does not currently enable highly complex models like those which may be more frequently leveraged in Financial applications.

Key Performance Indicators (KPIs)

Human Resources departments are rapidly improving their strategic reporting through the development of measurements that might have seemed impossible only a few years ago.  Some of the key measurements traditionally leveraged in HR over the past 5 to 10 years:

• Headcount
• # New Hires
• # Terminations
• Turnover Rate

Web-based SaaS software acceptance is enabling the use of an expanse of data that resides outside the typical HRIS.  The major drivers are:

As a result, companies can make use of data that was previously prohibitive to collect and synthesize.  We are now able to incorporate social networking, geographic, and labor market data freely available across the web to find and retain the best talent.

At Fuse Analytics, we are pushing the limits of these new analytic capabilities in Human Resources.  We help you transform the HR function into a strategic business partner by consolidating your HRIS applications and harvesting external data to provide an easy-to-use, 360-degree view under a single global reporting platform. Contact us at info@fuseanalytics.com to request a demo or learn more.

In this post I’ll cover some of the reasons global payroll and reporting has been difficult to achieve and some of the common misconceptions related to Global Payroll.

The Fuse Analytics Single Currency Exchange and Global Pay code catalog allow you to perform detailed analyses of your single biggest financial expense

Reporting:  Multi-Country Payroll platforms

There are a few major HR and Payroll vendors touting multi-country or global payroll software.  The reality of the technology is that each country payroll, even when well planned and implemented, still is not consolidated for cross country or cross currency reporting (except in receiving finance modules after consolidation on a ledger of accounts).  You may be able to achieve some cost savings by dealing with one single payroll vendor for a large cross-section of your geographies but if your main objective is to realize global payroll reporting then you should be wary.  Dig beneath the Sales facade, into the technical details, and you will typically find there is no simple solution to single currency reporting across country payrolls.  Some of the key obstacles are outlined below.

Process Harmonization

Net Payroll is one of the most difficult areas of HR to globalize due to the local regulatory influences on its calculation and disbursement.  The United States, being one of the few privatized health systems globally, often has complex plans (and tax rules which vary by state) around medical care.  The UK, with its complicated pension schemes is another outlier along with Italy and its lack of  seemingly constant payroll rules.  Although pay frequency is commonly monthly in most of the world, the US requires weekly, bi-weekly, and semi-monthly aggregations across reporting lines.

The effort to harmonize must first define what processes in Payroll can be made global and then, in turn, what pay codes to report on globally.  In most cases this is limited to common elements of pay such as Salary, Hourly rates, Bonus/Incentive plans,  and Time recording data.  It also must define the logic to be used in the calculation for metrics over different units of time.  These are decisions which are often company specific and are difficult to “productize” in multi-country payroll modules.

Data Volume

Transactional Relational Databases typically cannot handle the volume of data generated from Payroll without special considerations such as table indexing,  OLAP cubes, or non SQL based data warehouse technology .  A table of 1 Million (or more) entries can be taxing (no pun intended) for common SQL selection statements.   This is especially true if you are not running a finely tuned database leveraging one or more of those mechanisms listed above.  Payroll results can easily generate 10′s of Millions of rows in a single database table.  Consider the following example:

30 line items per pay statement per period

26 periods in a year (in US)

5000 employees

= 3,900,000 rows per year (19,500,000 for 5 years of history)

In addition to being US only, this accounts for the volume of “current” period values, not the usual additional triplicate Month-to-date, Quarter-to-date, Year-to-date accumulations of most of the same line items.  It is normal for users to want to subtotal, sort, and filter real time.  This makes reporting on the results difficult without leveraging special database tools.

Exchange Rate Synchronization

Exchange rates can be applied to local currency source data at a number of different integration points and using different dates (using period begin date, pay date, date posted to the financial ledger).  Therefore, getting a global reconciliation of each pay type “down to the penny” in a single currency becomes extremely tedious and even impossible in some cases.

Retro-calculations

Payroll systems such as SAP allow automated recalculation of pay periods into the past.  Although payments are usually carried forward for posting to Finance in current periods only, the underlying original period paycheck details are still subject to change once they have be recorded in the ledger.  Therefore,  careful planning around recalculations and posting must be considered before the two can be reconciled accurately.  Finally, a retro calculation coupled with exchange rate calculation aggravates the problem further.

All of the above make global payroll implementation a risky and often ineffective business case.  Even if the technical challenges are surmountable, the cost of a global payroll implementation and harmonization is very often too high to be competitive with local payroll specialists, especially considering the service trade-offs required.

Ask us at Fuse Analytics how we can help you achieve global payroll reporting at a fraction of the time and cost of re-engineering a global network of payroll providers.  info@fuseanalytics.com.

Easy access to your data from anywhere, even from your pocket

Fuse Analytics is the only HR Data Management as a Service provider that focuses solely on Human Resources. Our products and services are designed to Fuse all of your HR data, including from idle legacy applications and global payroll systems.

Our solutions reduce compliance risk and lower the cost of maintenance, all while enabling valuable insight into your data. For more information, take a tour and learn what Fuse Analytics can do for you.