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  • Writer's pictureGeorge J V - Stragiliti

10 result-oriented ways to transform reporting and analytics in IT/ Professional Services firms

Updated: Jan 9

If you are a CFO or CXO in in a Tech Services or a Professional Services firm, and you face challenges in reporting, analytics, budgeting, or planning then this article is for you.


For people and skill intensive organizations, the metrics and KPI’s that matter are significantly different from manufacturing, trading firms or other firms. If you are an SME, you face additional challenges to sourcing analytics you need. Here are some proven result-oriented techniques to make reports/ analytics for Tech. and PSA firms relevant, actionable, and yet affordable.


1.      The margin metrics that matter for Tech & PSA are calculated differently. Prioritize these.


Critical metrics like gross margin and operating margin are determined differently in Tech and PSA firms. The equivalent of Cost of Goods Sold (COGS) in normal accounting practice for service organizations for example is the Cost of Execution (COE).

Employee compensation is usually the largest chunk of such costs and need to be split based on what is applicable directly for executing a project/ service and what is not. Similarly operational expenses also need to be separated to direct and indirect costs. Only when such separation is done at the detailed levels will the financial metrics really make meaning.


Project and client profitability also give key insights. Displaying them with the right visualizations or calculating the net billing rate help in better decision making e.g., giving a thrust to contracting with the better clients and projects, and deprioritizing those that are a drain to the system.


2.      Operational metrics that matter for Tech. and PSA are different.


Operational indicators are also significantly different given the people intensive nature of the business. The most relevant ones are related to utilization and projected revenue.


Utilization Metrics.


Salaries form the largest chunk of spend and hence utilization of resources is crucial. Utilization is the best way to keep track and typical ways to do this is to measure metrics like

  • Billing utilization (percentage of time spent on billable activities)

  • Bench Costs (Unutilized time of billable resources)


Revenue Projections


Revenue projections need to be classified well to give deeper insights. Some insights for example are given below

  • Recurring services usually form the largest component, and one needs to ensure that this regular stream does not churn.

  • Tracking of expansion or mining (getting more business from existing clients) is a good indicator of customer satisfaction and indicates additional revenue without much marketing or sales costs.

  • New business or tracking of ‘hunting’ activities enables tracking of growth and indicates the results of sales and marketing activities.

  • Analysis of service line or region wise growth indicates specifics of how well each of these revenue sources are faring.

 



3.      People, Skill, and Retention related metrics are vital. Track and measure these


In Tech and PSA firms usually 20% of the resources or specialized skills give 80% of the revenues or margins and hence its crucial to nurture and track resource with skill availability. In the case of highly competitive environments, recruitment metrics also become vital. Some of the key metrics are


  • Resource availability by skill/ position

  • Billing and utilization by skill/ position

  • Time to Hire by skill/ position

 

4.      Sourcing data from multiple systems are difficult. Simplify and streamline these


Most tech and PSA firms run multiple systems for Accounting, Sales CRM, Marketing, Support, Execution and Delivery, Accounting, Contracting and Billing, HR, and Payroll. Extracting data from all of them with integrity is a challenge. Improved codification methods across systems, automatic cleaning of data and validation of the data prior to loading for analytics are critical and once done makes the analytics a lot easier.


5.      Make reports, visualizations, and analytics actionable


The way reports and metrics are presented makes a world of difference in enabling CXO’s to act on them. Some examples of better reports and visualizations are expanded below


  • Client Profitability – instead of listing them in a table, show them in descending order

  • Revenue – Instead of showing just revenue compare it against actuals, and project using good forecasting or machine language techniques it to the end of the period of year so that decision makers can see where things are going if not acted on.

  • Show depth to determine the root cause of the problem. Operational margins at the surface may be fine, but when broken down by Service Line, Region, Client, or Delivery team could help determine the root cause of issues.


6.      Focus on repeatability to track trends and correlations

 

Treating the activity of extraction, loading, and transforming (ELT or ETL) of the data as a pipeline rather than a monthly recurring set of activities helps. When data is sourced from multiple systems, integration utilities, loading and cleaning utilities are crucial. It is also important to track changes that occur in source systems since they require equivalent changes in the analytics.


By treating the data sourcing mechanisms as a data pipeline system, one ensures that the tools are structured, the processes are standardized, quality is built in, and the processes are streamlined.


Executives place more importance on the quality of the data being used for reporting than on the visualization bells and whistles, and once they learn to trust the pipeline, it becomes a source of right decision making.


7.0 Quality of tools used make a big difference

 

Tools for ETL and visualization are getting better. However, they need to be evaluated seriously from a capability/ performance/ support perspective and made available to the right teams so that analytics related work is delivered efficiently and with excellence.

 

 For SME’s however analytics is hard. Sourcing quality resources to work on such tools is expensive and its difficult to retain such resources. The option of moving to a single application like ERP is possible but that is a highly disruptive program that many SME’s cannot afford to execute. Many organizations actually prefer continuing working with best of breed applications in each area.


New options like Low/ No Code analytics pipeline applications like Stragiliti Smart Metrics combined with mature visualization tools like Power BI or Tableau can make a huge difference in affordability and speed. Self service options in implementation are now possible with such tools and platforms avoiding the need for costly consultants and reducing the cost of implementation.

 

8.0 Create a culture of data driven decisions

 

The positive impact of key decisions taken in time because of the availability of good data and analytics are far reaching. Some examples of data driven decisions which lead to better outcomes are given below


  1. Data showed that one service line was losing money. Action taken was to merge two closely related service lines and reduced the overheads for managing both the service lines. Net result was minimal impact on staff of the service line that was not profitable.

  2. Data shows that contracts with clients where margins. Action taken was to renegotiate contracts with lower margins and stop working with clients who insist on working with unprofitable terms. Net result – better profits, more attention to better customers.

  3. Data indicates that there would be a negative cash flow a few months down the line. Action taken – take an additional line of credit being opened or new investments raised for the gap. Terms of interest was much better when planned than taken in an emergency.


The culture of data driven decisions best initiated by senior management. During meetings communicating that data was used to take decisions help in giving credibility and objectivity to decisions. Data driven decisions are accepted easily and the decisions are better. It takes time for a data driven culture to get formed, but one can get there only when gets timely and consistent data that can be relied upon is available.


9.0  Combine them with good Goal alignment frameworks like SMART or OKR


Frameworks like OKR (Objectives and Key Results) and SMART (Specific. Measurable. Achievable. Relevant. Time Bound) ensure that company goals are aligned and acted on in a coordinated manner through multiple levels of the organizational hierarchy. All these initiatives depend on accurate metrics and KPI. A good combination of goal alignment frameworks, good implementation coaches, a well thought out program in place and good analytics can work together to provide results that can transform.


10.  Path to AI/ ML


Lastly – Artificial Intelligence (AI) and Machine Language (ML) is coming. Its getting easier and better allowing accurate projections and insights which could not be achieved before. This means that your competition could have an advantage unless you leverage it before they do.


However, there is one thing common in any AI/ ML initiative needs – good quality source data and aggregated data that can be relied on. Ensuring that you have a good data pipeline in place is the first priority since it is a pre-requisite. Start on it with urgency. The type of AI/ ML tools and resources to be used can be determined later.


Getting your pipelines, report visualization and planning/ budgeting mechanisms need to be in place immediately and these principles can be used to avoid wrong turns.


About the Author:


George John Vettath has worked for over 34 years in the enterprise software space having built and implemented software products and services across the globe. He has lived and worked in the US, Canada, Singapore, Australia, and India. He has experience across functions in development, support, marketing, product management as well as in running software companies. He has specifically worked in building and delivering Analytical, Forecasting, BI and Analytics products and projects during his career. He has a special interest in the role of low/ no code platforms in accelerating data pipeline and transaction systems, with a focussed interest in its applicability for small and medium businesses in the Technology and Professional Services space.


George is an Electrical Engineer from National Institute of Technology in India and has an MBA from the University of Sydney, Australia. He is currently based in Vancouver Canada, and is the Managing Director of Kallos Technologies Corporation, BC Canada (www.Stragiliti.net)

 

 

 

 

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