METRICS AND REPORTING: Using Data Analytics to Measure Risk/Reward
First and foremost, if there’s one thing I’ve learned in my now 18 years in professional services, it’s that you have to prove your relevant business value every day. Working inside a consulting firm, it’s assumed that good infrastructure teams will manage themselves like a business inside the business, accounting for and recovering their time against services performed… not unlike a client engagement. As the assembly of those engagements develops into a body of work, it then becomes paramount for business process leaders to rely upon the accumulated data to refine and sharpen the value proposition.
Though it may seem straightforward, using data to guide our decisions over the years has allowed us to find optimal resolve regarding seasonal workforce scaling and load balancing. For instance, each year we make careful study of the previous year’s peak period, including which client deliverables were finalized in which weeks to help construct our staffing schedules for local and national teams, contractors and even offshore team members.
Spotting trends is just as important. Our regular review of consumer behavior through metrics has sparked growth in our business and improved corporate outcomes in the past, including:
- reallocating existing staff to focus on new priorities like account management;
- placing bets with full-scale hiring for specialized creative skills; and
- developing and launching new self-serve digital tools.
In much broader-impacting scenarios, identifying multiyear trends in our data set has aided in our evaluation of vendor proposals to support various operational investments. Knowing your precise volume of work and frequency of need certainly can lead to the better leverage of your spend when it comes to contract negotiations of course, but when we used metrics to assist in selecting the right automated bindery options for our production fleet, we immediately triggered deeper cost savings. In other circumstances, we have isolated variables around outsourced work to encourage investment in desktop publishing versus creative resources offshore. One of the smartest things we do is use data to support our assumptions around signals in the business that require greater attention, often creating pilots for new service offerings or allowing us to workshop new processes around these indicators.
Data analysis has even helped us to set course for dealing with market disruptions. Has your organization ever been asked to adapt to a business merger or acquisition? I can’t tell you how very important the power of intelligence around your body of work can be when needing to quickly adapt service teams to new priorities based on an action of the parent organization. The availability of straightforward analytics around presumed capacity has proven to be critical for us in making necessary business cases for additional resources.
In other instances, the careful study of previous hours associated with a specific type of work, rather than the simple isolation of the volume in net units, has prevented us from considering unnecessary staff reductions when new government regulations posed a significant threat to our model.
In wrap up, the looking back and looking forward at our business and the use of data has proven time and again to be a differentiator in our ability to succeed as an in-house provider. In fact, it’s difficult for me to imagine how a data-poor organization can truly succeed in demonstrating stakeholder value.