Will HR projects ever be financially measurable?

It’s impossible to measure the financial value of all HR focused projects.

While there may be exceptions like reducing annual leave liability or reducing insurance premiums, overall HR projects fail to have a real dollar benefit associated to them. Often this means that the HR projects are approved because decision makers intuitively feel that there will be benefits but they do not believe in actual business case.

Instead of using intuition to approve HR projects you can you Value Driver Modelling (VDM) to assess the real financial benefit of a project. Using a VDM-based tool, like the  Value-Driver Psychological Assessment Tool (VD-PAT), you can specifically assess which parts of the project are going to affect which areas of the organisation and so in turn make an assessment as to the financial improvement possible from the project. Not only does this allow you to assess the value of the project by its self, but you can also assess the value across a whole portfolio of interrelated projects

The diagram below provides an overview of how information flows into and through the VD-PAT:

Value-Driver Psychological Assessment Tool VD-PAT

Organisational inputs provide the information required to understand how an organisation currently operates.

Inputs are collected from surveys, interviews, observations and enterprise data. The inputs may be for a single point in time or could be collected regularly over a period of time to track changes. Inputs may be collected for a specific team or unit within the organisation. Inputs may also be collected for certain aspect of an organisation, for example, job characteristics only.

Organisational Improvements are the improvements made to specific psychological factors within the organisation as part of a HR project.

An improvement may impact one or more psychological factors. It’s then possible to follow the impact that these changes have on organisation through empirically proven correlations and relationships.

There are two external inputs that provide information to the tool.

  1. Peer organisational data is used to compare organisational performance as well as provide information for further research.
  2. Research from peer-reviewed, empirical studies is used to find the inter-relationships between different organisational psychological factors. This informs the development of the Value Driver Model, which in turn drives the formation of the Value Driver Engine.

Within the tool there are three main modules of processes that take inputs and transform them into valuable outputs

  1. Consolidation is the module that takes raw data and transforms it into information that can be feed into the Value Driver Engine. This ensures that the outputs are meaningful and accurate.
  2. The Value Driver Model is the framework of causal relationships and correlations that determine how organisations actually function.
  3. The Value Driver Engine is the powerhouse of the tool that combines the input information with the Value Driver Model to produce valuable outputs.

There are 5 valuable outputs from the VD-PAT.

1. Insight

Insight is the culmination of the value available through all the outputs of the tool. Specifically, insight takes the current state of the organisation and identifies a portfolio of improvements and strategies that match the organisation’s need. Additionally, it also produces a thorough business case based on the costs of implementation and the resulting benefits expected from the change.

2. Benchmarks

The engine can benchmark the relative performance of the organisation against peers in the same sector or geography, or compare them against the entire population of data available to the tool.

3. Diagnostics

Results can be used to define the nature of the organisation allowing decision-makers to understand the main factors currently contributing or detracting from the organisation’s performance. Results can also be used to compare different organisational units to understand what contextual or psychological factors are impacting their relative performance. Lastly, results can be used to reinforce or dismiss anecdotal theories describing what is affecting the organisation’s performance.

4. Benefits Tracking

When a change has occurred in an organisation to improve its performance, the resulting change can be compared to baseline figures as well as tracked overtime. This can provide evidence as to the success of the improvement, or act as an early warning that additional intervention is required to ensure that the improvement meets its expected improvement goals.

5. Value

Ultimately, the purpose of the tool is to equip decision-makers with the information and insight they require to improve how the organisation performs.

Try it yourself

I’ve built a very limited prototype of the VD-PAT based on Job Characteristic Model (JCM) theory. You can download a copy of the prototype here in Excel.

The first spreadsheet (1. Research) provides you with an overview of the Job Characteristics Model (JCM), which sets the structure for the second spreadsheet (2. Model). The model transforms the JCM into a VDM. The third spreadsheet (3. Survey) provides some external input data for our model while the fourth spreadsheet (4. Consolidation) takes the results and make them readable to the 5. Engine. This engine calculates the results from the survey so we can see how satisfaction, growth satisfaction, and motivation are affected. The spreadsheet ‘7. Benchmark’ combines the peer organisation data from ‘6. Peer Organisation Data’ to allow you to benchmark your own performance.

To follow the process from start to finish, start with Research spreadsheet. Note that the collective relationships are carried across to the Model spreadsheet. Next the Survey collects key information from the participant (this can be aggregated to be more than one person). Next the survey data is combined into a structure that can be read by the VD-PAT on spreadsheet Consolidation (this is generally a more important step for when you’re not using excel). Then Engine combines the survey results with the framework of the JBC theory to tell us what benefits we’d expect to receive. This information can then either be benchmarked against other organisations or can be used to value, in terms of dollars, the likely benefit (Intervention).

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How to optimise your casual workforce through Tableau visualizations

The use of casual employees in Australia has been stable at about 20% of the workforce for the last two decades. These employees provide an affordable means for businesses, both small and large, to employ a contingent workforce. In turn, it provides employment opportunities to a range of people who would not be able to work otherwise, such as teenagers getting their first jobs through to stay at home parents wanting to supplement their household income. Casual employment is generally associated with a higher hourly rate (compared to their permanently employed peers), no provision for annual or sick leave (long service leave is an exception), and no notice for termination. While casual employment has a lot of benefits, there’s a hidden cost and risk if the nature of a casual employee’s work resembles a permanent job.

If a casual employee is working regular, ‘systematic’ hours, the law may construe their employment as permanent, making the employer liable for additional costs. One of these costs could be the financial liability for annual and sick leave (despite the fact that employee was paid a higher rate) as well as damages for unfair dismissal.

If you really need some of your casual employees to work like a permanent employee you can investigate alternative employment options like part-time arrangements. There are new types of part-time employment available these days like flexible part-time (e.g your ordinary hours of work may be averaged over a period of one to four weeks) and partial part-time (e.g work full time for 9 months and have the other 3 months off). In combination with traditional part-time arrangements, this provides employers with plenty of options to balance their resourcing requirements and the availability and desired flexibility of their staff.

While part-time arrangements provide a way to mitigate the hidden cost of a casual workforce, it helps to know which employees are in danger of working regular hours. One useful tool is a casual hours dashboard designed to 1) categorise the risk of employees based on their pattern of work, 2) allow you to drill down to the day to day details of their work and 3) then respond by changing their work shifts or employment arrangements accordingly.

The image below shows the landing page for a casual hours dashboard. Across the top are the count of casual employees that fall into the three risk categories for ‘regular’ employment. The meaing of ‘regular employment’ can change from organisation to organisation so the definition is included below each category. Below the definitions are the filters so that leaders from different areas of the business can confine the report to their relevant casual employees. Along the lower right is a spark line for each employee showing, at a high level, the pattern of their hours for each fortnight. This provides a way to assess how regular they are working and for how long.

reuben kearney - casual hours dashboard - first page
Click to zoom

From this landing page it’s possible to ‘zoom’ into the daily work hours for each employee. By clicking on an employee’s name you can move to the drill down page pictured below. This page provides a lot more detail as to how many hours an employee works, across which days and over what duration. It’s possible to load this drill down for all the employees that work for a certain manager or belong to a certain risk category.

reuben kearney - casual hours dashboard - employee drill down page
Click to zoom

The final page in this dashboard is a fortnightly summary of hours for all casual employees belonging to a certain teams. While dashboards are built to be interactive, it’s not always possible to work with your clients in front of a screen. This final page allows your to print out results from a page optimised for A3 printing.

Click to zoom
Click to zoom

If you seen another way to visualise this issue please let me know, also let me know if you have any questions about how you might be able to implement a similar solution for your organisation.

Unleashing the Internet of Things for Brisbane

A community initiative in Amsterdam, led by an organisation called, The Things Network, crowdsourced a complete city-wide ‘Internet of Things’ (IoT) data network in six weeks using a new technology named LoraWAN. There’s every reason why a similar project should succeed in Brisbane.

IoT is a low bandwidth, long range alternative to WiFi and is the latest development in Internet technology. IoT allows everyday objects (e.g. tracking devices, detectors) to have network connectivity so that they can send and receive data.

The internet was created by people connecting their networks and allowing traffic to pass through their servers for free. As a result, there was abundant data communication and exponential innovation.  A LoraWAN network in Brisbane could achieve the same outcome for the Internet of Things by creating abundant data connectivity so applications and organisations can flourish.

How could we do it?

Leverage new technology

LoraWAN (Long Range Wide-area network) is a new data network technology that allows for connection to the internet without the use of 3G or WiFi. Such technology is perfect for the Internet of Things as the devices that connect to it have extremely long battery life (up to three years), and the stations are long range and low bandwidth. Imagine a network that can be used without cumbersome WiFi passwords, mobile subscriptions, and zero setup costs.

LoraWAN at a glance:

  • Device batteries last 3 years
  • Stations range are 10 kms
  • No monthly subscription required
  • Low data bandwidth

Build with low-cost infrastructure

Since the network’s reach is widespread and the cost of the equipment is low, covering an entire city can be done with a small investment. The city of Amsterdam was covered with only 10 gateways at a cost of AUD$2,000 each.

Community involvement and owners through crowdsourcing

Since the costs are very low we do not have to rely on large telecommunication providers to build the network. Instead, we can crowdsource the network and make it public without any form of subscription. This project can be built by the users, for the users.

How an IoT network could be used in Brisbane

An IoT network provides the foundation from to build an entire ecosystem of interrelated devices and applications to transform the way residents, visitors, and businesses live, work and relax in Brisbane. The following are a number of examples that demonstrate how this network could be used.

 internet of things - brisbane - citycycle  internet of things - brisbane - buses  internet of things - brisbane - bins
Increase bike use

Track the use of CityCycle across the city to identify key user groups to increase patronage.

Provide a tracker to residents so that they can receive alerts if their bicycle is stolen and assist in relocation. Use the same trackers to better plan future cycling facilities.

Improve the patronage of buses

Track buses to provide alerts to passengers so that they know exactly when to expect their next bus.

Use the same trackers to receive greater detail on bus journeys to improve the modelling of bus timetables and routes.

Reduce maintenance costs

Add weight and chemical detectors to bins so that they are emptied exactly when needed, saving on maintenance costs.

Detect cigarette bin fires as they occur so that Council Officers can respond quickly.

 internet of things - brisbane - tourism  internet of things - brisbane - parking  internet of things - brisbane - crowdsourcing
 

Enhance visitors experience

Provide devices that pair to visitors’ smart phones to provide directions to areas of interest and commentary on historical landmarks. Understanding how visitors experience Brisbane allows better design of facilitates to meet their needs.

 

Optimise parking

Detectors for street parking can provide accurate records of when parking bays are being used. This can be broadcast to show drivers where there are free parks. Council can use this information to accurately model optimal parking times and costs as well as forecast future parking requirements.

 

Drive community development

Since such a network would be open and free, anyone can develop apps that connect to the internet through it. Through open, crowdsourced development you would see the rapid introduction of new apps and devices for the people of Brisbane to use to improve their lives.

Getting involved

A project like this provides a unique opportunity for Brisbane to lead the world in the adoption of LoraWAN technology. With minimal cost, over 10 kilometres of the city can be covered. Additionally, with extensive involvement from the community, local developers can leverage the network to build a whole ecosystem of new devices and applications. If your interested contributing or being updated about such a a project, let me know.

How to build a social contract for your agile team

Team charters, team principles or vision statements are nothing new. If you visit enough work places you might see old A3 posters printed out with some clipart and an acrostic poem spelling out R.E.S.P.E.C.T. These type of statements have a purpose – usually in the early days to support a new team – but without any updates they don’t mean much.

The purpose of a social contract is to document how a team wants to work together. It should balance inspirational statements with the details of actual behaviours and attitudes that the team want to see. A key advantage in having a social contract is that by defining what the team should look like, the team will start to consciously and unconsciously exhibit those behaviorrs and attitudes. Read more

How you can benefit from workforce analytics

Many startups and small-to-medium sized businesses operate without workforce analytics, essentially sticking with traditional human resource strategies to run that side of the business. While technically there’s nothing wrong with this, the use of workforce analytics has a profound impact on the businesses that use them.

Workforce analytics refers to the data aggregated by a combination of methodologies and software to apply statistics to employee data. The real benefit here is that business management professionals can use this information to optimise human resource management, creating a more efficient, cohesive organisation.

HR metrics promote value driven initiatives that grow businesses. Not only are businesses able to see illustrated statistics for current trends, but human resource professionals can simulate “what if” situations. When businesses can move past simple HR numbers, they can see how the company is doing as a whole. A key benefit of workforce analytics is the ability to see exactly what is and isn’t working and make changes within the organisation to augment success.

An evolving business is a thriving business, and the use of workforce analytics in human resources is a key to the success of the organisations who use them. When a workforce’s effectiveness can be measured, investigated, and defined, a company can change policies, procedures, and roles as needed.

For example, human resource professionals can investigate the effectiveness of new employees as opposed to those who are nearing retirement. The results of this data will give the company an idea of the scope of job functions, training, pay, and more.

Companies who use workforce analytics for their human resources operations enjoy higher efficiency as a whole. From the ground up, these statistics can be used to shape and define an organisation’s mission, direction, and future success.

Self-service analytics roadtest: Watson Analytics vs Tableau vs Popily

If you teach someone how to fish…

The world of analytics has exploded with a vast array of new technologies, tools, systems, training, opportunities and business models. Most people understand that analytics is powerful and have heard stories about how companies like Amazon and Google use it drive innovation and grow their organisations. However, when it comes to your own life, its can be difficult to understand exactly how you can use it. For some, analytics feels like its something akin to magic wielded by ‘data scientists’ with PhDs and decades of experience.

The reality is that analytics is being democratised by the very same technology that’s made it valuable. This has given raise to self-service analytics. After years of investment in centralising data, maturing data governance and user-friendly software there are now a range of options for anyone to answer their own questions using sophisticated analytical techniques.

There are a lot of tools available to anyone to do you own analytics. Some are ‘one off’ tools like Google’s Ngram viewer that will allow you to investigate how frequently specific words have been used in books or Twitter Analytics which will let you look over the stats for your own account. Then there are more broader tools that will allow you investigate a range of different data sources. While there are many examples I want to focus on three across the broad spectrum of options. They are Watson Analytics, Tableau and Popily.

Who’s who

TL;DR

  • Watson Analytics is cloud-based, lets you explore your own data, you can explore your data by typing natural language questions and it’s available with tiered payment options starting from free.
  • Tableau has desktop, cloud and server-based options, its optimised for Enterprise data sources, and has free and paid options.
  • Popily is a brand new offering and will continue to mature through new releases, it’s cloud-based, and currently only uses publicly available data but is free.

Watson Analytics

You may recognise the name ‘Watson’ as the artificial intelligence developed by IBM that won the quiz show Jeopardy in 2011. Watson was able to listen and respond to natural language questions beating two previous champions. Today, Watson is able to analyse large corpora of unstructured data allowing it to manage decisions in lung cancer treatment, find new food combinations for recipes and make music recommendations.

The Watson AI that is able to do all this is not the necessary the same ‘Watson’ you have access to as part of IBM’s cloud-based Watson Analytics offering. Watson Analytics allows you to ‘ask’ questions about your data sets in natural language by typing it questions. Watson Analytics responds with options and graphs that it’s determined will best answer you question.

While there appears to be no move to provide a desktop version of Watson Analytics, IBM’s enterprise-grade business intelligence offering, Cognos, is inheriting some of Watson Analytics natural language processing and visualisation aesthetics. For a great overview of the product, check out this video.

Tableau

Tableau is best known as a visualisation tool. Its adoption within the business community continues to grow year on year. Tableau is a mature offering and recently released version 9. It can be deployed on your local machine, your server or from the cloud. It allows you to create beautiful, interactive graphs to quickly and intuitively tell a story or to provide insight into previously unintelligible data. To get a sense of the look and feel of Tableau’s visualisation check out their gallery.

Popily

Popily is a brand new offering released by the same team responsible for the analytical-themed podcast Partially Derivative and who developed CrisisNET. Popily provides non-technical people the ability to explore data without needing to know code or statistics. As a brand new offering, the cloud-based Popily can only be used to explore publicly available data sets added to their platform. I believe the release of Popily is the start of a wave of new start ups with a focus on self-service analytics leveraging the raise of technologies like software-as-a-service, machine learning and scalable analytics.

Let’s test them

I’ve reviewed these offerings by the following areas:

  1. Signing up
  2. Loading data
  3. Finding insights

The data we’re looking at has been limited to what’s currently available through Popily’s public library of data sources. We’ll use Airbnb’s data set because they share their listing information through a Creative Commons license. In fact, you can explore the data through their own visualisations here (created using Leaflet and Mapbox).

Signing up

All three offerings have a free option (so feel free to jump in yourself and have a play – Watson Analytics, Tableau Public and Popily). Creating accounts for all options is straight forward, although you’ll need to download software for Tableau.

For Watson Analytics, if you pay you’ll be able to analyse more data (more rows and columns) and there’s an enterprise version where you can allocate access across a tenancy. Actual prices and packages are constantly changing (at least the time of writing) so check out the site for the latest prices.

Tableau has paid options designed for enterprises and are structured around the number of licensed users. For companies this means you’ll be paying for both desktop versions and a server license so that you can privately share your visualizations. Specifying users can be a bit limiting if your an organisation that prefers to have flexibility or plan on managing security access through Tableau server.

Loading data

Watson Analytics allows you to upload your own data and, if you upgrade, you can also connect automatically to the Twitter API (they’ll grab a 10% sample of tweets for the last 6 months based off keywords). Adding data is as simple as clicking the add button from the login dashboard. The free account is limited to 50,000 rows and 40 fields. Adding an abridged version of the Airbnb data set took about 6 minutes over a medium speed NBN connection. Once uploaded, the first thing you’ll notice is that Watson Analytics has assessed the quality of your data. When you first click on your data set you’ll get a dialog box with a series of prompt questions.

self-service analytics - watson analytics - prompt questions
Click to zoom

Tableau is optimised to analyse large data sets. For Tableau Public, it can connect to Microsoft Excel, Microsoft Access, and text files. While you are limited to 1 million rows of data, this is only a limit per connection. There is a file size limit of 1 gigabyte to save to the cloud. Adding data connections is easy as you can select by source type (e.g Excel file, database, etc), you can view the data once connected, and select how you want to import the fields.

There is currently no ability to load your own data sets into Popily. This is why we’re using the Airbnb public data set already added to Popily. They are extending invitations to companies to add their data now.

Finding insights

The focus on this section will be looking for relationships between the price of accomodation and the number of rooms.

As we saw when we first loaded our data set, Watson Analytics is already suggesting areas that we might want to investigate. If you select the Explore option you’ll be able to ask you natural language questions. In this instance I’ve asked ‘what is the relationship between bedrooms and weekly_price?’.

self-service analytics - watson analytics - search by room and price
Click to zoom

Exploring these options I found that the visualisations are not all that useful initally. Watson Analytics likes to aggregate by average and it hides a lot of the information you want to see. However, clicking on the columfunction on the right allows you to select exactly what fields you want and how to graph them. Using this I created the following graph.

self-service analytics - watson analytics - price by bedroom by property_type
Click to zoom

This is graph is more meaningful. I can see the relationship I’d expect to see between price and the number of rooms. But now I can also see which properties attract a higher premium per room (in this instance it’s trains and boats). Now you can also quickly click on the property_type field and select other relevant fields to investigate like Country and Neighborhood. Another powerful option available through Watson Analytics is its prediction engine. To see more about this feature check out some guides here and here.

self-service analytics - watson analytics - prediction dialog

Tableau is much more hands on then Watson Analytics or Popily. This means that when you first add your data set, you’re not going to get any automatic recommendations. However, Tableau has done a lot behind the scenes. It’s categorised each of the Airbnb fields and determined if they are attributes or dimensions. This works in your favour when deciding how to visualise your information.

self-service analytics - tableau - first screen
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From this starting screen you can start to explore your data. To explore the relationship between beds and price you grab the fields from the lists on the left and drag them across to the row and column shelves. Tableau will automatically select the scatter plot chart, which, for this investigation is exactly what we want. We can now decide which detail we want to split the plot by. Dragging across the property type field, and aggregating by average values, we can replicate a similar graph to what we create in Watson Analytics.

self-service analytics - tableau - second screen
Click to zoom

From here there’s a lot of flexibility with what you can do with this information. You can add dimensions to change size, shape and colour. You can also quickly add filters, trendlines and, forecast if you have time series data or graph data to a map.

self-service analytics - tableau - third screen
Click to zoom

When you first log in to Popily you’ll see a list of recent public data sources on the right. Click on Airbnb listings and you’ll immediately be presented with a set of charts. If you scroll to bottom you’ll see that the data source has been prepopulated with 2,421 pages of charts. You can go through and explore these pages, but it makes more sense to start limiting your search to those fields that you are interested in.
self-service analytics - popily - first log on

Let’s start our search with the relationship between cost and the number of rooms. You can search by fields within the yellow bordered search dialog at the top of the screen. Select monthly price and number of beds. You’ll see the number of pages has been limited to 5 and you can start exploring charts more relevant to your investigation. You’ll be presented with a chart called Average monthly price by number of beds over date cost started on AirBnB. Once again, not a particularly insightful. If you scroll down you’ll see Average monthly price of number of beds.

self-service analytics - popily - search by average better result

This graph is a little more useful as we can start to see the relationship – namely, more beds more expensive. However, from the example picture above you’ll notice an immediate limitation of Popily’s visualisation. There’s no axis headings, no legend and no labels. In fact, other then the heading the only indication you’ll know what you are looking at is if you mouse over the graph elements. Even more annoying is that if you have multiple elements on a line graph it won’t label the values (you need to guess) and you need to be very precise with how you position your mouse to get the values.

Conclusion

I like Tableau because it provides the most control over how you load, model and visual insights. However the value of self service analytics is giving anyone the power to do meaningful analytics. From the perspective of non-technical user I’d recommend Watson Analytics. It’s a more mature offering than Popily and doesn’t present you with learning curve required for Tableau. I’m looking forward to seeing how these offerings continue to grow and evolve. If you agree or disagree let me know below.

The top 5 ways to analyse diversity in your organisation

Being diverse and inclusive is essential for any modern organisation. Without a reputation and strategy for accepting employees from a range of backgrounds your organisation will fail to fill vacant, critical roles. Without those roles you will struggle to build innovative products and services, deliver complex strategies in an unpredictable market and forge resiliency into your workforce. Despite these clear benefits only one-third of Australian organisations believe that being diverse and inclusive would support their market growth and customer satisfaction (see Diversity Council Australia).

The following 5 techniques provide a way for an organisation to thrive by fostering a diverse and inclusive workforce. Read more