Anna Grome – Principal Data analytics has become a prevalent topic of interest in organizations over the past decade. Organizational leaders are increasingly recognizing the competitive advantages that stem from being able to make data-driven decisions to improve performance and business results. Whether used to identify top talent, optimize customer experience, enhance efficiency, or improve overall organizational performance – when used right, data analytics is a key enabler of business strategy and can provide vital information to inform decisions and shape action.
Data analytics comprises a wide range of techniques and applications – the approaches used to make a movie recommendation or to predict the movement of a market are not likely to provide the insight needed to understand human performance in a complex work environment. The sub-discipline of people analytics is characterized by a motivation, scale, and methodology that sets it apart from other forms of data analytics, and it begins with a fundamental question.
There are several reasons organizations decide to measure. At TiER1, our CEO consistently conveys the message that we “measure to align.” We use data to align on priorities and where to focus our energy. We’re also continuously experimenting with new ideas, new organizational structures, and new rhythms and routines. Measurement allows us to capture data to determine what’s working, what’s not, and what’s enabling us to offer the greatest value to our clients, our associates, and our community.
In addition to using data to align, I’m a big believer in the idea that data helps us in our communication. It forces us to make our beliefs explicit. If someone thinks investing in a specific marketing campaign is a worthy endeavor, identifying what “worthy” means – and to whom it is valuable – is important. Is it more visibility? More meetings? New meetings with new prospects? Increased win rate? Making the implicit belief explicit helps us communicate and then take action as things unfold. – Greg Harmeyer, TiER1 Performance CEO
Organizations also use measurement and analytics to gauge progress and areas of opportunity related to specific people-based initiatives. For example, clients often ask for our help in assessing:
Gauging whether the initiative has moved the needle in the desired direction – at the desired scale – is used to determine when and where to pivot or change course altogether.
Finally, some organizations use measurement as an intervention. Measuring particular behaviors, mindsets, and/or results can foster reflection, convey a message of what’s important and valued, and focus associates’ attention on what matters relative to the organization’s strategy and mission.
Some organizations don’t yet have a data-driven culture, are uneasy about metrics and analytics, or simply don’t know where to start. The truth is, measurement isn’t rocket science and doesn’t have to create uneasiness. But it does require some important steps that shouldn’t be overlooked. It also requires attention to some common pitfalls so they can be avoided.
Here are the important steps:
1. Align on the questions you want to answer. What are the questions central to your business operations? Your talent? Your culture? What hypotheses do you have about the impact of interventions such as onboarding, training, or standardized work processes on business outcomes? Address how those questions – or answers to those questions – map to your business strategy and objectives. If the connection isn’t apparent, ask yourself if there are better questions that will yield data insights that are more closely connected to your strategy.
2. Consider who needs these answers. Who is the target audience for the answers? Senior leaders? Functional leads? Individual performers? How will the data be used and for what purpose? Will it be used to prioritize initiatives? To determine hiring needs? To determine opportunities for greater efficiency? Ensure the intended use of the data aligns with your core values and principles.
3. Determine indicators. What are the leading and lagging indicators – or metrics – that would help you answer the questions you need to answer? In most cases it won’t be a single indicator, but several indicators – both quantitative and qualitative – that will help tell the story.
4. Determine whether the data is already available. In some cases, the data to answer your central people and business questions may already be being captured in an existing system (e.g., ERP, CRM) or via an existing mechanism (e.g., Employee Engagement Survey, Customer Satisfaction survey, etc.), and it’s a matter of mining that data. If the data is not already being collected, determine a means of collecting data (e.g., survey, interview, focus group, observation) – and weigh its value relative to the time commitment it will require of your associates.
5. Collect the data. Perhaps you have a sophisticated analytics platform in place; perhaps your organization isn’t quite ready for that investment. There are myriad ways to collect data, including open-source tools such as Microsoft Forms and even SurveyMonkey, and a number of proprietary tools. If you don’t have a sophisticated platform for analytics, don’t let that deter you from gathering the data you need to answer your central people and business questions.
6. Analyze, interpret, and tell the story. Data is only a collection of (sometimes overwhelming and disconnected) numbers, quotes, dashboards, or reports until you convert them into a meaningful narrative that the audience can understand. Use a combination of visualization and narrative to explain what the data reveal, why it’s important, and how the insights can drive decisions and meaningful action.
7. Activate the insights. Investing time in analytics has little value unless it’s ultimately used to drive decision making, action, or change. Analytics isn’t an end point; it’s the starting point for bringing your people and business strategies to life.
Even as we ride the crest of a digital transformation in business, it is important to note that data by itself is not a panacea. There are some guardrails that should be recognized to avoid the pitfalls that are often obscured by the promise of big data. As you create a people or organizational analytics strategy, create a data-driven culture, and embark on the practice of analytics, it is important to keep the following in mind:
1. A single data element rarely tells the whole story. The real world is complex, measurement is imperfect, and hidden causes are everywhere. It’s extraordinarily rare to find a 1:1 mapping between a behavior or intervention and a performance outcome. Rather, a range of contributing factors are typically at play. If decisions are made based on statistical regularities and algorithmic output alone, without true causal understanding or understanding the array of contributing factors, unfortunate and sometimes unethical missteps are possible. Quantitative metrics or “hard” data should be combined with qualitative data – e.g., “voice of the employee” or “voice of the customer” data – to gain more texture and understand the “why” behind the data. [That is, keep the “people” in people analytics.] Qualitative data can be used to craft better or more targeted questions and hypotheses to explore. This will help you understand a situation, issue, or problem more fully.
Zeroing in on one metric too much can be dangerous…turnover, ENPS, utilization, profitability…any of them alone can be misleading. Usually it’s the combination of data elements that tells the best story. – TiER1 Performance CEO Greg Harmeyer
2. Focus on trends instead of a single data point. Because a single data element rarely tells the whole story, avoid fixating on measurement results from a single point in time. Trends are often more meaningful. If your quarterly employee engagement survey score is 70 out of 100, what does that actually tell you? It’s more meaningful to recognize that the engagement score has steadily increased from 55, to 61, to 70 over the last 3 quarters. Identifying trends and patterns in the underlying data will provide you with the most useful insights into the general direction over a period of time, and can alert you to questions to ask, areas to explore, and potential interventions that might be needed.
3. Issues of data quality can destroy the usefulness of your analytics. You’ve likely heard the phrase – “garbage in, garbage out” – and this applies wholly to analytics. Without good governance of the data, analytics are useless and can actually do damage. Attention needs to be given to setting up a good governance structure – including who will own the data, who will ensure quality and accuracy, etc.
4. Not everything that can be measured is meaningful. With the influx of “big data,” many organizations are swimming in a sea of data without clear questions to ask of the data, and clear means to make sense of the data. Don’t waste your energy on data that don’t help you answer your core questions. Sometimes simpler “small data” is better. Let your core business, people, and performance questions drive the data you seek and make sense of.
5. Some of the most meaningful things to measure are the most difficult. Some of the “softer” or “squishier” characteristics of talent are harder to quantify and may have a more indirect impact on performance and business outcomes. For example, if a consultant hasn’t brought in a multi-million dollar contract – but has mentored, coached, and ignited innovative ideas and thinking in those who have – use quantitative metrics about contracts closed with caution. Don’t underestimate the use of listening to your employees and your customers via interviews, focus groups, and surveys to help assess behaviors, mindsets, and value that may be less quantifiable, but nonetheless value-add.
Some organizations are already highly sophisticated in their data analytics processes. For organizations who are just getting started, there is an opportunity to take small steps in the direction and expand from there. People and organizational analytics doesn’t have to be rocket science, and can help you answer a variety of questions related to people and performance within your organization. They can be a strategy enabler – helping you make data-informed decisions about where to focus your time and energy – unleashing potential and accelerating achievement of your desired business outcomes. Yet they should never be used in a vacuum and should always be considered in the context of “softer” data gleaned by listening to your workforce and your customers.