“Big Data” and “Data Science” have become big buzzwords, influencing trends in the digital economy of today’s world. CXO’s are, every day, more aware of its importance and the role these skills play as a competitive advantage in the market, supporting a better, more accurate, faster and even automated decision-making process. Decision-making is becoming more dependent of factual data, whether it reflects, deterministically, business rules or just show, empirically, customers’ behaviour and market patterns that can be used to influence loyalty, product development or marketing strategies, among other areas.
Data analysis as part of a wider landscape of business intelligence and decision support systems is all about taking the right decision, in the right moment with the right insights, and it has been like this for a couple of decades. So, what has change and why are we now so conscious of its value?
First, the market has evolved, competitors are eager to gain market share, the digitalization has changed the landscape and there is less room to fail or to postpone the right decision. Bringing automation supported in facts – not only samples but the entire data gathered around a business – can be a distinguishing factor that may put you a step (or two) ahead of your competitors.
Second, data characteristics has change and the number of systems that can produce and collect data has increased dramatically, giving us a new conception about the changing world that we experience every day. Data is being produced at an astronomical pace and it is estimated that 90% of the data in the world was created in the last two-three years. The term “Big Data” can be defined as data that became so large that it can’t be processed using conventional methods.
For example, taking a practical approach today, when performing commercial analysis, the insights are no longer about sales based in invoices or purchase orders. It’s about everything a customer does, from the moment he feels a need to the moment it decides to buy something and get it delivered at his door. We are talking about interactions with websites, comments and recommendations he has asked about a product in social media, the propensity that he may have, based on similar behaviour from other customers, to react to promotions or alternative products with different value proposition.
To talk about “Big Data” is to be able to classify data sources according with the 4 V’s and have the right visual and logical tool to interact with them. The 4 V’s of “Big Data” are:
- Volume. The sources of data and collection points have become so common and available that we need better technology and strategies to process data as a whole and infer knowledge from pattern recognition, regressions, clustering, map reduction and other techniques to process huge volumes of data.
- Velocity. It is not only the volume of data but also about the pace. And while data is being generated at high speed, it will demand decisions to be made faster – in some cases, real-time decision making – with a shorter lead time, able to produce results and to influence the market.
- Variety (Complexity). The variables and correlations among them – not always clear or able to be perceived from a human rational point of view – led the new generation of techniques and applications to focus in multi-dimensional analysis and geo-spatial representations, to bring more sense to the business analysis being supported on top of the data.
- Veracity (Value). This refers to the trustworthiness of the data. Can the decision agent rely on the fact that the data is representative and will he challenge the quality of the insight or outcome that is being provided? Trusting the data and the model is a critical step towards automation and to be even more efficient in a competitive market.
Functional Areas
Taking the right decision, in the right moment with right the insights is what INTELIX team does best. Our team has a vast experience of decades working in the business analytics and data modelling landscape, with very successful implementations in functional areas such as:
Inventory management, optimization and yielding
Automatic replenishment and in-season analysis
Marketing activity, recommendations, cross-selling, etc.
Customer classification and clustering
Loyalty programs definition
Sales teams’ optimization
Dynamic yield in front-office systems, including B2C and B2B channels
Suppliers performance and contracts’ negotiation
Production planning and capacity optimization
Throughput maximization
Simulation and what-if scenarios
The tool and the technique will always depend on the customer, the need and the expected outcome of our services and application. But, for most common cases, it will probably result in the successful implementation of functional applications and tools to serve these purposes:
