Business Intelligence & Predictive Analytics Solution

  • Data Mining

  • Data Conditioning

With predictive analytics, you can use your data to recognize trends, predict future events, and identify business risks and opportunities.

Business intelligence spans technologies, processes, procedures that collect, integrate, analyse, and present data generated by businesses and their customers. Unlike traditional data analytics tools, Business Intelligence present insights in a more coherent manner.

Optimising service delivery is one of the primary applications of predictive analytics in business intelligence. Businesses can create a better customer experience by studying past behaviours and preferences, and customising their service offerings to better suit the particular needs of each customer.

Predictive analytics can also help businesses keep the news cycle going during off-season months. Smartphone manufacturers, for example, identify months in which phone sales can slump due to loss of press, and release minor refreshes, new colours, or software updates to the existing models to keep the headlines talking about their products.

Our BI & Predictive Analytics solutions work best in tandem with our data architecture solutions and help your organisation maximise the value of its data.

Priorities while providing BI & Predictive Analytics solutions

  • Providing rich data insights across all your various data silos
  • Identifying relevant performance KPIs and providing visibility for those KPIs
  • Converting raw data into rich data by implementing effective ETL layers
  • Providing access to intelligent analytics to all key stakeholders

Principles of our BI & Predictive Analytics solutions

We strive to build systems & processes that will maximise the value derivable from your organisation's data. The 9 foundational principles that we follow are,

  • Use a requirements-driven approach. Even when using off-the-shelf information models, requirements must drive the solution. Plan to go through multiple iterations of requirements gathering.
  • Investigate & fix DQ problems early. Data quality issues make it difficult to integrate data into the analytical environment and can make user reports worthless. Start with data profiling to identify high risk areas in the early stages of the project.
  • Build a metadata-driven solution A comprehensive approach metadata management is the key to reducing complexity and promoting reusability across infrastructure. A metadata-driven approach makes it easier for users to understand the meaning of data and to understand how lineage of data across the environment.
  • Leverage existing infrastructure. Business Intelligence implementations should align with a company’s IT strategy and vision for how IT will support the business. BI should complement existing IT capability which can often be achieved by leveraging current IT infrastructure to provide the back end, and using business intelligence solutions to provide the front end.
  • Establish a centralized governance structure. A business intelligence implementation can touch every area of an enterprise. It requires cooperation and shared ownership from the business and IT, new data management protocols, strong project management and ongoing analysis of the metrics used.
  • Use an agile, modular approach. Achieve more flexible and more effective implementations of business intelligence faster with agile development and by focusing on specific areas that are guided by an integrated, overall strategy.
  • Focus on the right metrics. Metrics must be aligned with the company’s strategy and capabilities, including both internal and external inputs, and consisting of both leading and lagging indicators.
  • Make “structured human” decisions. The most interesting relationship involves “structured human” decisions, in which human beings still make the final decision, but the specific information used to make the decision is made available to the decision-maker in some enhanced fashion.
  • Keep it simple. Even though technology might enable us to drill down 16 levels into the data or slice and dice it 100 different ways, that kind of analysis may be irrelevant and distracting.

Too often we forget that genius, too, depends upon the data within its reach, that even Archimedes could not have devised Edison’s inventions.

Get in touch with us for an initial consult and know how we can help your organisation maximise the value from your data and generate predictive analytics.

Data Architecture

Big Data, Machine Learning