Editor’s Note: This piece was first published in the 22nd PERFORMANCE Magazine – Printed Edition. The KPI Institute’s Business Research Analyst Aida Manea discusses in this article how AI supports decision-making by eliminating biases and diminishing the number of human errors.
Through a variety of ways, artificial intelligence (AI) can help organizations enable and focus on better decision-making. AI could take over administrative roles and allow humans to prioritize more valuable things that require more time. The intelligent agent can take over manual tasks and enable process automation. AI can also plan decisions or predict results based on historical data.
That holds true even at the departmental level. Freeing managers from worries related to repetitive, administrative, and employee compliance tasks gives them more time for performance management activities. This is reflected in the use of AI as a behavioral assessment tool, data-driven processes where teams are coordinated through feedback, and more opportunities for meaningful human interaction.
AI in Performance Management
According to a study conducted by the University of Twente, there are two ways to implement AI in an organization. On a small scale, AI can assist a manager in improving small parts of the system, like inventory optimization. On a larger scale, AI could play a role in redesigning core processes at the organizational level.
One thing to pay attention to is knowing which type of implementation to choose. In an environment where human interaction and feedback are essential, it would not be the wisest choice to go for the second option as it could affect human connections.
The best-case scenario is to benefit from an assisting AI as it would help the manager make decisions while the assistant processes a vast amount of data. This will not only speed up the decision-making process but also guarantee the data veracity.
AI makes its mark on performance management systems through digitalization. Real-time feedback is important now more than ever due to the changes within performance management. The traditional yearly review is now being replaced by more frequent and informal check-ins as this would enable the shift from talking about people to talking with people. The 360-degree feedback practice focuses on asking colleagues for feedback on an employee’s performance.
Another strong point of AI is that it eliminates the biases toward individuals by assessing patterns and historical data with no opinion that might dilute decisions. While the line managers or HR may have their personal opinions about employees coincide with their responsibilities, AI supports decision-making by eliminating biases and diminishing the number of human errors.
AI for HR
At the HR department, the implementation of an AI system will not only process the data faster but will also deliver robust data collection, frequent fact-based performance, and improvement discussions. HR managers are responsible for their teams’ attitudes and behavior so that they can truly contribute to organizational goals.
Efficient and effective recruitment – A recruiter’s main challenges are prioritizing all the roles they are responsible for and finding a way to differentiate among candidates that applied for the same position. Deploying AI determines how long a job requisition will take to fill based on past data so that recruiters can prioritize the roles available.
Moreover, AI can predict future performance by determining the match between a resume and the job requisition and filtering candidates. The challenge in IBM regarding effective recruitment is to help HR managers surface the top candidates for the open positions and prioritize the most important requisitions. Their solution is IBM Watson Recruitment, an AI system that assesses information about the job market and past experiences of potential candidates in order to predict the necessary time to fill in positions and spot the most suitable candidates.
The huge advantage for recruiters is that they can focus on building and nurturing relationships with applicants. At the same time, AI collects the demanded skills from job requisitions and generates a score against skills mentioned in resumes. Finally, IWR watches over the hiring decisions to make sure they are free from bias and turns the candidate and recruiter’s experiences into better ones.
Enhancing motivation – At IBM, the individual needs of employees are essential, and managers get alerts about it. For example, the manager is alerted when there is an employee with years of experience in the company, has skills, and is ready for a promotion. The same applies to the case of employees with a higher propensity to leave or when employees from a specific department are at risk of missing their targets.
Through this alarm signal, managers are able to make decisions over the organization’s talent management approach. Another AI implication is the chatter analysis used to capture the top three internal issues from social media sources. Leaders can receive personalized recommendations to increase the team’s engagement. Other benefits brought by AI can be smarter compensation planning and career development.
The drawbacks of AI systems can be avoided by making sure the data is never used as a sole determinator in decisions.AI initiatives can barely break organizational barriers, based on a survey conducted by Harvard Business Review in which only 8% of firms engage in core practices that support the adoption of Artificial Intelligence. The shift towards AI should start by aligning the organizational culture and the internal operating ways to support digital transformation. Here are the three main actions to scale up AI:
Replace siloed work with cross-functional teams collaboration. The mix of perspectives increases the impact AI has over the processes as it ensures that projects address broad organizational concerns and not just isolated ones. Moreover, if end users are required to test what development teams work on, the chances of adoption increase.
Abandon the top-down approach. Integrating AI into processes will increase the trust of employees in algorithms. They are the ones who will ultimately make a decision based on the algorithm result and their experience. Once they feel empowered to make decisions without having to consult a higher-up, they will get a taste of what AI can offer: freedom of action.
Embrace an agile, experimental, and adaptable mindset. The idea of having an idea baked before it is deployed must be replaced with a test and learn vision. This reduces the fear of failure and allows companies to correct minor mistakes before they become costly ones by receiving early feedback from users.
AI’s ability to promote automated processes, analyze data, predicts trends, and even build frameworks helps the organization in its strategy and business planning. In order to maximize the product and effects of AI, it is essential to establish a strong strategy mindset.
The business intelligence and analytics industry reached over $ 19 billion globally in 2020, albeit the derailed economic performance caused by the pandemic. The business intelligence market growth experienced a 5.2% increase, and the data analytic growth rate is expected to rise in the coming years as companies realize the need to manage data to make better decisions.
According to Angela Ahrendts, a former retail Vice President at Apple Inc., customer data is the most significant differentiator among businesses in this era. Companies that know how to maneuver heaps of data to create strategic moves usually succeed. To determine how companies adopt and implement data analytics, let’s first understand how data can make a company’s operations efficient.
Data Analytics: Four Ways to Increase Company Performance
As discussed earlier, data analytics is beneficial for making more accurate business decisions. Managers and executives can take action on the data insights they get to drive better competitive advantages in their markets. There are four ways data analytics can accelerate business performance:
The first way is by creating informed decisions. One of the key benefits that businesses look out for when dealing with data analytic solutions is developing better and more accurate decisions from the insights they get from analyzing data.
There are two processes that ensure the development of better decisions: predictive analytics and prescriptive analytics. Prescriptive analytics are utilized to project the way companies react to forecasted trends, whereas predictive analytics focus on events that might occur after analyzing collected data.
Improving efficiency is another route. Data analytics is highly beneficial especially in the operation management for streamlining operations. For example, companies can retrieve and assess their data relating to supply chains to discover where delays in their supply networks happen or to forecast areas where problems emerge and use these insights to prevent any issues.
Data analytics also enables risk mitigation. To cut down losses, data can be utilized to reduce physical and financial risks in business. Through collecting and assessing data, inefficiencies can be either identified or predicted. Also, potential risks are revealed to inform management on creating preventive policies.
Lastly, data analytics enhances security. As many businesses confront numerous data security threats in today’s era, it is essential to keep the company’s cybersecurity out of dangerous attacks that cause financial or brand image blow. A company can evaluate, process, and draw insights from its audit logs to showcase the source of previous cyber breaches. The outcome of this exercise would be to recommend possible remedies to the problem.
Join The KPI Institute’scertification course on data analysis today to learn more about data analytics, improve your analytical skills and make wise business decisions.
Gone are the days when analyzing and visualizing data to get information was a job that was limited to the IT and business intelligence (BI) divisions. Gone also are the days when the sole possession of knowledge, skills, and tools for data processing was in the hands of the “data guy.”
Data is becoming more and more abundant and essential for various business operations. This makes centralizing data processing on one or two divisions an inevitable bottleneck. On the other hand, analytics and visualization tools are becoming easier to use, with more intuitive user-friendly interfaces that require less and less technical expertise.
What SSBI Is About
Self-service business intelligence (SSBI), also called self-service data exploration, has become an important approach for data-driven insights in business. It means giving the ability to the wide range of employees who are not experienced with data to drive insights from relevant datasets and create exploratory visualizations to help them better understand the data and to use it in reports. It’s also a part of what is called data democratization if you’d like another fancy term on the plate.
It should be, however, distinguished from the second approach called dashboarding. While the latter should still be the responsibility of the experienced BI team, turning amounts of data to finely curated reports on the most important KPIs within a well-developed narrative can happen. The SSBI approach aims to:
Avoid time delays in data-driven decision making among the low and mid-level teams that may happen due to the centralization of analytics responsibilities.
Minimize intuition-based decisions that can be made by low and mid-level teams on a daily basis due to lack of analytical capabilities.
Enhance internal communication within the teams by making data-driven insights and visualizations easier to generate, and therefore more frequent integration of reports.
Enhance external communication of the organization as the insights and visualizations can also be easily used in developing publications, like blog posts for example.
Google Sheets and Datawrapper
There are tons of visualization tools out there that can enable you to create an SSBI system for your organization, some of which are technologically advanced, but each has its best uses and downfalls.
Just like Google Sheets and Datawrapper. The advantages of using these tools are the following:
– Businesses with no capabilities of experienced teams or infrastructure can implement the system.
– Anyone can use it as it requires little to no technical expertise.
– Visualizations can be easily duplicated and edited, suiting fast-based work routines.
– Visualizations can be easily well-formatted and laid out, leading to efficient reporting.
– Generate both interactive and static visualizations that are suitable for embedding in various forms of reports, from web-based all the way to paper-based.
Using a self-service BI solution can help streamline operations and support critical decisions. It also encourages collaboration, simplifies daily business needs, and increases one’s competitive advantage. With the efficiency brought by SSBI, businesses can focus on what matters most to them.
Want to understand how visual representations can support the decision making process and allow quick transmission of information? Sign up for The KPI Institute’s Data Visualization Certification course.
Decision-making and strategy planning are considered the most important managerial functions. One cannot simply exist without the other. Planning refers to the intent of choosing a future course of action, whereas decision-making implies selecting a course of action from the alternatives. The interrelation between the two functions creates the process of strategic decision-making, which entails the efforts before and after a choice has been made.
With the strategic decision-making fueling the management process, every organization can model its own by imprinting it with the corporate culture. That means every company attempts to create a better mousetrap to dominate the market through common values, attitudes, and beliefs that ultimately influence the way decisions are made. Eventually, the strategic decisions are considered rational if consistent with the corporate objectives.
The strategic decisions and list of action items are the outcome that derives from the regular performance review meetings held by the Strategy Office and Department Heads. These gatherings aim to monitor the organization’s progress in achieving its objectives by looking at KPIs’ results and initiatives’ status.
Through strategic, tactical, and operational decisions, companies form a habitual way of doing things. As a result, the employees get a sense of behaving and acting in certain situations.
Different techniques are applied depending on the data analysis type encountered. During the data gathering and reporting stages, specialists often encounter the analytics data. Several approach techniques found in practice would be using statistics on historical data to identify data patterns, forecasting methods or regression, and correlation analysis.
During the performance review meetings, the executives benefit from the analytics, and a root cause analysis is conducted to identify the real source of issues, the moment in which the situational analysis starts to take on color. Before any decision is made, several root cause techniques are conducted, among which are the Ishikawa diagram and the 5 Whys.
By using the Ishikawa diagram, managers can identify the many possible causes for an issue. By listing the main issue and finding its possible root cause matches, a fishbone diagram’s results serve as a map of problem-solving situations.
The 5 Whys gives the specialists the chance to understand how relevant asking questions is. The approach is rather simple. Whenever a problem pops up, ask “why?” five times to clarify the nature of both the problem and solution. The answer has to come from informed decisions. The decision-making process should be based on an insightful understanding of what is actually happening on the work floor.
Residual uncertainty is what haunts many executives. But what is that, and how can it affect strategic decisions? After grueling hours of conducting the best possible analysis, there remains one gram of uncertainty. Either that may materialize into a new product in development that cannot ensure 10% in net profits or the outcome of an ongoing negotiation. However, there is always quite a bit of uncertainty around the corner that can fissure the best strategic decisions agreed upon.
Under the umbrella of uncertainty, traditional approaches pursued in strategy planning can turn out to be a spike for failure. Disparaging uncertainty can lead to strategies that do not defend against the threats nor take a chance to discover the opportunities brought by it.
In order to hedge this risk, agile decision-making techniques are addressed. Some of them consider their risk tolerance by evaluating the consequences of each alternative and apply Second-order Thinking. Others choose to perform a comparative analysis of alternatives, gather input from each team member, and then share perspectives with the whole team. This is called the Decision Matrix. In the stage of documenting decisions, the Decisions’ Log technique is applied, while in the stage of communicating decisions, there is the DACI matrix (Driver, Approver, Contributors, Informed).
Initial and horizontal alignment as part of strategic planning
After the decisions have been agreed upon, the key phase is to translate the corporate goals into objectives for each business unit. This completes the circle and brings us to the finish line of the strategic decision-making process. At this final stage, the traditional cascading approach may cause discrepancies between the objectives and projects from each business unit. To prevent this, a horizontal alignment is performed.
In other words, the managers, together with the strategy office, need to cascade corporate objectives, KPIs, and targets at operational level. All conflicting initiatives or objectives need to be addressed. The last step is setting in place prioritization criteria for selecting initiatives to see what gets approved and what is not.
High quality data can play a huge role in increasing efficiency and improving performance and can help managers in the decision-making process. Sometimes, it is acceptable to make decisions based on instincts and gut-feelings, but the majority of them should be backed up by numbers and facts.
Data-driven decision-making is a process of collecting measurable data, based on organizational goals, extracting, and formatting data, analyzing the insights extracted from it, and using them to develop new initiatives. Nowadays, advanced software is available to help with data gathering, processing, reporting, and visualizing, to support managers.
The main steps of the decision-making process
The first step to build a well-functioning, data-driven decision-making process is to clearly define organizational goals, and to identify the questions to which the answers we find can help reach these goals. For example, if our company’s revenue goal is to increase its portion of the market share by 20% until the end of the year, a good question would be: what are the most important factors which have influence on market share?
The next step is to identify data sources and to find custodians. The source of the data highly depends on its type. There are qualitative data, which cannot be expressed by numbers, and quantitative data, which can be measured by numbers. We can collect data from primary and secondary sources. Primary sources can be observations, interviews and surveys, whilst secondary data can be collected from external documents, third-party surveys and reports.
The third main step is to clean the gathered data. During the data cleaning process, raw data is prepared for analysis by correcting incorrect, irrelevant or incomplete data. There are six data quality dimensions which should be kept in mind, during this process: Accuracy (indicates the extent to which data reflects the real world object), Completeness (refers to whether all available data is present), Consistency (refers to providing the same data, for the same object, even if this data appears in different reports), Conformity (consists in ensuring that data follows a standard format, such as YYYY/MM/DD), Timeliness (indicates whether the data was submitted in due time, respecting the data gathering deadline) and Uniqueness (points out that there should be no data duplicates reported).
Only now, the data analysis process can start. Statistical models should be used to test data and find answers to the business questions identified beforehand. Descriptive statistics can help to quantitatively describe and summarize features of data and to describe, show or summarize data in a meaningful way. For example, monthly sales or changes in employee competency levels can easily be presented in a visual manner.
Interferential statistics can help find correlations between different variables and predict future outcomes. For example, by using regression analysis, we can make a prediction on how growth, experienced in the employee competency level, can positively affect the sales volume.
Even if the data gathered is cleaned and correct, and the data analysis process has respected all the recommendations above, if the data is not presented in a meaningful way, it will not be of much use. Well-presented information and the outcomes of the analysis can help in interpreting data, thus supporting the decision-making process.
From time to time, data should be updated and re-evaluated, to make the best decisions in today’s continuously changing business environment.
The advanced analysis techniques and software, which are available nowadays for the majority of organizations, make it possible to build up a data-driven decision-making culture, which leads to more prudent business decisions. These tools generate more thoughtful decisions that help performance improvement, which ultimately lead to organizational growth.