By Bala Cuddalore, February 11th, 2017
Metrics are measurements of tasks performed by employees to produce, deliver products or render services in a given enterprise. It can also be measurements of actions taken by customers as they engage with any company, its website, their products or use their services. Metrics allows enterprises to track efficiency, performance, quality and even customer satisfaction.
Metrics can also be defined as measurements created when employees, customers, vendors perform their actions or tasks that are tied to the execution of business processes tied to the business model of the enterprise.
As an example, an enterprise manufacturing cars, will have to procure raw materials, possibly source and / or manufacture parts that are delivered to an assembly plant. The car is then assembled in a production line and then tested before being distributed to dealerships and showrooms. The enterprise then gets paid based on sales. This is their business model in a simplified sense and its employees perform various tasks to execute this business model.
To measure the efficiency of the tasks performed by its employees or to track how the company is performing, there is a need to measure. And this measurement is made possible through metrics.
Metrics are by products of data collected through automated systems as employees work on their tasks or when customers interact with systems to report on the actions they are working on or have completed.
Organizations define metrics based on what they want to measure. It is usually to track top line and growth related activities or events. But sometimes it has to do with bottom line related events that allows companies to save money. And more relevant than ever before Customer satisfaction has become an important metric.
Typically, top line growth is measured by the number of customers a company acquired during a time period or the amount of revenue they recognized.
Bottom line measurement is usually to do with much money or resources a given company saved while executing their business tasks such as manufacturing or operations. Some of the metrics include gross margins or net profit.
While these are more at the corporate level, individual teams can also measure their performance and efficiency for their individual department or function. For instance, a customer service team can measure how effectively they are handing customer complaints or phone calls. Or an operations team can measure how quickly they are able to turn around customer orders and are fulfilling them.
Similarly, a manufacturing company can have many metrics sprawling their entire organization. They can adopt Lean or Six Sigma methodologies to see how they can upkeep and improve quality of their products or minimize wastage.
Therefore depending on the line of business or industry, metrics are defined based on the need to track the performance, efficiency and or quality of company, its products or services and / or teams, business units within.
While a vision of a company is established to corral its employees towards its future, it is often necessary to track its performance on the road towards the vision.
And the road usually milestones that the company must cross towards its journey. These milestones tend to have metrics tied to them to ensure that they have hit the milestone successfully. Such metrics are measurements tied to corporate goals. They are loosely called Targets or OKR’s (Objective Key Results).
For instance, an Internet company can set a milestone to reach 1 billion user registrations by a certain year. And to track this milestone, User Registrations becomes a metric they would need to track.
Corporate metrics can also be tied to target goals such as Revenue or achieving a certain level of quality or even Customer satisfaction.
Corporate goals are set by the executive management of the company along with input from the Board. And the set of metrics that are defined to measure the progress made towards corporate goals become corporate metrics.
Now that does not mean that individual teams or departments cannot have their own set of metrics alongside corporate metrics. Those department level metrics can also co-exist. It would be wonderful to although tie department level metrics to corporate metrics or goals as it would allow employees within a specific department to see how their work impacts the company’s performance.
Once a set of metrics tied to corporate goals are set, these metrics are then tracked periodically to ensure that the company, its departments are on track to hit those milestones.
These metrics are reviewed by the management and the board in periodic meetings. Typically at the board level, it is done once every quarter in what is sometimes called as a QBR (Quarterly Business Review).
However executive management and business leaders who are key decision makers within an enterprise do look at their metrics more regularly so that they can make decisions that allow them to steer their company towards their goals and also avoid setbacks, hurdles. In the internet age, management looks at metrics at least every week if not daily. And sometimes even intra-day if an event warrants for that kind of scrutiny such as a big product launch.
Metric computation and governance begins with an enterprise coming together to define what metrics they want to define and standardizing them across the company.
This activity is usually done by business and more particularly by Finance or Operations. Once the business leaders agree on what their teams or business units or the organizations must measure and track, Technology takes over the gathering of data and the metric computation to prepare the metrics.
Once metrics are prepared in terms of data, Business Intelligence and/or Analytics teams produce dashboards, scorecards that render the metrics defined in a consumable format. These dashboards are provided access to across the enterprises.
For metrics to be shared and accepted across an enterprise, the stakeholders or keepers of these metrics must govern these metrics. Governance of these metrics includes standardization of metrics, their validation for accuracy and monitoring to ensure they are not compromised over time.
To see how your company can benefit from driving the right metrics towards your corporate goals ... sign up for a demo of nlightn - The Analytical intelligence Platform
Hope this provides an overview of what metrics are and their purpose. Thanks for reading & your comments!
-- Bala CuddaloreTop
By Bala Cuddalore, January 17th, 2017
In the promise of becoming data driven decision making entities, more and more enterprises are building their next generation data platforms with improved data infrastructure to capture their ever growing data. With the growth of data, integrating data and building value out of data to fulfill increasing business needs is becoming exponentially complex for data engineering, BI and Analytics teams. It is not that these teams are not doing a good job of preparing data, surfacing intelligence and adding value. It is more a question of how timely and actionable their insights are to business. These insights must also come with a level of urgency and priority for the business leaders to leverage this knowledge in making effective decisions. Decisions that eventually result in growth, increased revenue, increased customer satisfaction and or possibly even prevent losses.
Presently, data engineering combined with the infrastructure team deliver data platforms that serves data from various source systems within the enterprise. The BI team with the help of data engineering integrates all data from within and outside the enterprise. Upon this data foundation, the BI team constructs metrics and KPI required for reporting to various stakeholders across the company. Having disseminated data, the analysts find trends and find out why things are happening the way they are. Finally the data scientists, prescribe and recommend what needs to be done to drive a certain business goal. Looking at a number of enterprises and how these teams are organizationally structured, they work mostly in silos and tackle individual problems or specific initiatives as opposed look at enterprise metrics as a whole.
With this being the basic construct of how data, BI, analytics and data sciences teams function, the expectation is that the data travels through the various teams within the organization and into the hands of the decision makers in a timely manner as actionable insights. This is where the true value of data is expected to be harnessed.
However, contrary to the above expectations, the value of data is somehow diminished by the time it reaches the decision makers and business leaders. The challenges can be summarized as follows:
As a result, the business leaders are always looking in hindsight as the value of data captured as illustrated above is diluted by the time it reaches the stakeholders or is too late. It is not that the required data or metrics is missing or not available. It can be under the very noses of the entire enterprise and they still would have missed it. It is more so a problem of scalability of these teams who have to constantly look at numerous metrics, their trends, their associations and spot new behavior to bring that to the attention of their business leaders with a level of urgency.
Therefore with the growth of data, BI and Analytics teams will find it harder to provide the timely insight and value to business. Hence key insights with the right priority are often missed or too late for the decision makers to make timely changes.
In order to solve this problem, enterprises would have to look differently at how they can create value from data with the right priority.
To begin with, data has to be distilled to extract the essence and be made into easily digestible snippets. Secondly, observations of the data must be translated into behavior of various metrics and how they are related to one and other.
Having understood the behavior of metrics and their trends, one has to identify opportunities that have growth potentials and / or caution the decision makers of any negative impact if any.
Finally the message must be delivered on a platform that is intuitive from where it can be easily consumed.
What is Analytical Intelligence ? Coined by a renowned American psychologist, Robert Sternberg (Tri-archical Theory of Intelligence), it is the ability to analyze and evaluate ideas, solve problems and make decisions using analytical, creative & practical giftedness of individuals.
How does Analytical Intelligence apply to Data, BI, Analytics & Data Sciences ?
The parallel that can be draw to this is that, as much an individual has to apply various types of intelligence to be well rounded, we as data proponents must advocate use of all the tools and arsenal to better aid business leaders to be data driven and make fact based decisions.
Apart from the technologies that are available to source, transform, integrate and store data (small and big data), we must particularly deploy methodologies and processes that we use to make data more valuable to business.
The processes & methodologies being referred here are:
Business Intelligence (BI) - Creating BI from data by integrating and aggregating data points from several sources to provide insight on what is happening using a single source of truth.
Analytics - The observations made through BI must be subjected to further analysis on why it is happening. Using hypothesis and proving it right or wrong with analysis is a must to determine what is useful to business versus not.
Data Sciences - And finally, from data sciences, machine learning tool kits, create practical models to automate delivery of value added insights to business leaders to help drive decisions where it most matters.
A new breed of platforms called as Analytical Intelligence solutions, will bring BI, analytics and data sciences into play to leverage data to the fullest.
Analytical Intelligence platforms of the future will build BI from data by integrating multiple data sources within and outside an enterprise into a single holistic view. Single source of truth is the closest that is analogous to the holistic view term. Beyond the holistic view, the AI platform will allow organizations to define and configure metrics and or KPI based upon some agreement from all stakeholders and business leaders.
Using this holistic view, these AI platforms will break data into smaller snippets that can be packaged to deliver the maximum business value. This is still business intelligence and there is nothing new but extracting data nuggets with maximum value becomes critical. This cannot be done without analyzing data.
In order to distill the sea of data into data nuggets, AI platforms will run automated machine learning algorithms to perform analysis that is required to observe trends within the data and extract the essence. This is building the analytical portion of the solution. What are the trends being observed? What are the reasons?
These AI platforms will go on to tap into more complicated algorithms to reveal any associations, and causality for the trends that will then be captured along with the degree of importance and significance.
To determine the choice of the algorithm and increase probability of causality and predictability, the AI platforms of the future will bring all tools in the data sciences tool kit to the problem. Having applied the most relevant machine learning algorithms to identify patterns and increase probability of causation, the raw data will now be enriched with business value and be ready to be presented to the business leaders and decision makers to recommend and prescribe actions.
Finally presentation of all of this value added information into succinct messages requires nift packaging. The consumption of these nuggets of information will also highly depend on using an effective delivery platform. Relevancy and timeliness are critical to ensure that the right information is delivered to the right stakeholder with an enterprise based on role, business unit and or function.
Using Analytical Intelligence and combining the strengths of BI, Analytics and Data Sciences processes and methodologies, a new breed of Analytical Intelligence platforms will extract what is most important to business and provide value from data that is unprecedented. In doing so, the four challenges that were outlined above can be resolved.
To recap the challenges and how they would be resolved:
Since all the value delivered out of data is from a single source of truth, the entire enterprise will be looking at KPI and metrics where there will be no two interpretations of the data.
Timely delivery of succinct messages combined with business intelligence, analysis and data sciences built on top of pristine data will allow the decision maker to focus on what really matters and take necessary actions.
Finally with basic intelligence and analysis already taken care of, analysts can scale better with the understanding of where their efforts should be focussed and business leaders can drive their companies by pushing the right levers in the areas that are most impactful to their goals.
In closing, only with Analytical Intelligence can enterprises address the challenges we face today with extraordinary growth of data and address the inability of current day tools to discern actionable and timely insights. Analytical Intelligent systems are the future wherein data combined with BI, analysis and data science tool kits will be deployed to enable organizations to become completely data driven and make decisions that set them on a trajectory to greatness.
Find out more on how the Analytical Intelligence platform from nlightn Technologies can help your company stay on top of your metrics.
As always, thanks for reading & your comments!
-- Bala Cuddalore