Nowadays, data is one of the most valuable corporate assets and Softinsa, together with IBM, is working on the development of methodologies, approaches and technologies to enable this asset, in order to facilitate the consolidation, understanding and extraction of value.
Softinsa has an extensive experience in implementing Business Intelligence (BI) and Analytics solutions. It recognises best practices and new types of information and trends in this challenging new data era.
How we do
With a wide base of qualified professionals, Softinsa is currently working with several customers in the whole data life cycle, developing activities related to BI, including Data Modelling, Data Architecture, Data Integration, Data Quality, Data Governance and Reporting. In recent years, we have developed solutions applying Data Mining, Machine Learning and Cognitive Algorithm techniques leading trends and needs in the market, regardless of the sector.
Softinsa can provide the skills and experience to bridge the gap between business and IT needs, addressing the general information needs of any organisation.
Softinsa currently has a local team of widely acknowledged consultants with experience in all business sectors, with the support of IBM Labs. The teams include Business Analyst Consultants, Data and IT Architects, Data Analytics Consultants, BI Developers, Data Engineers and Scientists.
We are prepared to respond positively to the challenge of developing and maintaining current applications and solutions and defining a transformation journey, together with our customers, that puts the field of Business Intelligence and data at the service of each process and function of the organisation, enhancing the optimisation of what already exists and is to be maintained (operational excellence) and the transformation, evolution, creation of new solutions and offers for what must be modernised (new sources of revenue).
An integrated offer
The Data Warehousing capacity aims to help customers start a new data-based culture that incorporates information and analysis in all aspects of their business, from operational to tactical and strategic use.
Our team can manage, plan, design and implement a centralised sustainable and reliable DW, a single point of truth, where data are complete, clean, reconciled, understandable, replicable and traceable, which is crucial to ensure quick and accurate responses to corporate and regulatory needs and to compliance requirements.
The Data Governance capacity aims to provide customers with a more effective procedure to manage their data. It provides a robust way to control data flows and cycles and is an accelerator to support processes that need to know where, how and what data is being used, produced, accessed and saved.
It’s absolutely critical to enhance effective data quality, data usability, data integrity, data security and data preservation policies, as well as to make access to data natural and available to any employee of the organisation through the publication of real-time data dictionaries and statistics about its status.
The Analytics and Reporting capacity aims to provide a wide range of features that aid Customer business units to access and interpret data, explore information, create and automate reports, dashboards, KPIs, among others, supporting effective business decisions and simplifying business analysis, as well as monitoring and measuring operational performance.
We are tool agnostic and develop using corporate suites such as SAS, Microstrategy, SAP BO, IBM Cognos, BI self service solutions such as Click view and sense, powerBi, but also open source and freeware frameworks in cloud and on-premise versions (Microsoft Azure and AWS, among others).
The Discovery and Insight capacity allows customers to search for data in advanced analytical desktops following approaches ranging from visual data discovery to machine learning. Its ultimate goal is to allow the customer to enter data science modernisation by applying statistical analysis techniques, data mining, machine learning, among others.
Understanding data, finding hidden information, discovering patterns, building predictive models, such as time series models to predict sales, using machine learning to build models to predict events such as churn, fraud, or building models to extract implicit and correlated information from data sets, such as consumption patterns and cluster identification.
