In this Digital Age, every decision takes origin from data: operational drivers can influence tactical management alternatives during the short terms activity, while the strategic representation of the company can affect the shift of the long-term direction towards new life cycle phase of the organization.
As a business analytics firm, SDG‘s focus must be the design, the development, and the diffusion of these foundations in the decision-making process.
This is the core of our service, this is the most valuable asset we can provide. This is why we play.
Spread data culture, discover hidden data, change data into Intelligence, give value to data. These are the values of SDG.
Spread Data Culture
Building a permanent platform that helps the customer acquiring knowledge from data, and taking the right decision. Using a method that helps the project to settle the right value of the data. It seems intangible but it deals with our chance to establish a long-term relationship with the client organization. It is not only crunching the numbers, it’s to have a culture which pushes to make and measure the success of decisions only with data and never without.
A data-driven culture is a workplace environment that employs a consistent, repeatable approach to tactical and strategic decision-making through emphatic and empirical data proof. Put simply, it’s an organization that bases decisions on data, not gut instinct. (P. Ramaswamy).
SDG can take the opportunity to help companies to start the journey for a Data Culture transformation, giving the right direction both technical and functional to the client to set up the change management needed to put in place this transformation with its service portfolio and resources’ competencies belonging to the different consulting profiles that are part of the teams.
With a simple infographic, giving the “stylists room” access to the Fashion Collection performances of the previous season, the number of model variants cancellation decreased by 10% in a season.
Discover hidden data
Every day we have the possibility to let the customer think about new data to analyze, data which for many companies are brand new but for us, as consultants are “just hidden”. Our second goal is to insert a Data Discovery Phase in each project - even in finance, where one could think to be aware of everything. This could even be the first mission of a project in some contests where the company is searching for something which is not captured by the current systems but instead resides “nearby” or aside the environment.
A typical example are the so-called missing sales which with the current technology, in particular for the e-commerce product or services, they can be intercepted collecting the queries which have been submitted to the website and resulted in no answers by the company. Or, on the manufacturing side, all the sensors data that resides on local databases mainly near the product line controllers, that can be integrated into a common platform and used in the statistical or predictive analysis to help maintenance or operation planning to take a decision even in near real-time.
Connecting analogic lathe machinery through an IoT hub to measure the electrical power consumption instead of the rpm, a brake system manufacturing company decreased the shop floor running maintenance costs by 15%.
Change data into intelligence
An important step is to change data into intelligence. When we invent a KPI our goal is to transform it to the best way to represent a business phenomenon and give the right intelligence to the manager that is using it in his decision-making process. No need to be particularly complicated, the simpler the better.
An eyewear company increased orders completeness in wholesale by 1% last year. It has been achieved just using a Salesforce dashboard with an algorithm which showed and proposed to the sales representatives the available “similarities” in terms of product characteristics - when the desired SKUs were not present.
Give value to data
Finally, trying to weight the value of data is one of the new assets of the company which led to loss prevention and other initiatives to protect them to fully exploit potential. During each initiative, the benefit in using data must be highlighted as one of the main deliverables; plenty of data has not to be misinterpreted as a decrease of their intrinsic value for the project sake.
During data preparation for an analytic project, we discovered that the predictive power of Census Data coming for free from US government was higher than other expensive sources, saving the company some dozens of thousand dollars of the yearly fee.