We aim to get you insights quickly and painlessly and our experience has shown that success hinges on synthesising your business knowledge with our analytics skills. We work collaboratively; we don't just build models but first take the time to properly understand your business. We start with two simple questions:
What problems do you want to solve? and
What data do you have access to?

By understanding your organisation and the practical decisions it makes, we can advise how your data could inform decision making.

Examples include:

  • A membership or subscription business that wishes to know which customers are most likely to churn in the next 3 months
  • A retail business that wishes to understand the optimum way to allocate marketing spend
  • A service organisation that wishes to be able to predict the demand for its services so it can ensure it has enough staff available
Our work typically falls into these distinct areas:

Improving the Visibility of Data

Some of your most valuable data might be sitting in a set of excel sheets on the laptop one of your employees. Other data might be in the cloud, but only a few people have permission to access it. If combining these two datasets could yield additional insights, think how much more value could be gained if all your data could be utlised by all who might need it.

Auditing what data exists and how it might be unified into a single source of truth, securely stored, regularly updated and then shared are important steps in the data journey. Developing a data warehouse will encourage collaboration across the organisation, stimulate innovation and, in time, deliver efficiency and boost productivity.

Preparing the Data for Business Use

The data will likely have been collected from multiple sources and will need to be cleaned, transformed and combined into manageable formats.

To do this, the organisation may need to adopt and implement data tools and utilise cloud-based platforms. Legacy systems and technology should be assessed if they are appropriate for the future ambitions for data in the organisation. Teams may need to upskill to use the new tools and additional resources may be needed to execute new tasks. Blooming Data typically advocates the use of technology-agnostic solutions that work with your existing technical set-up.

These changes will inspire new ways of working, new processes and new 'data-centric' cultural norms. Team members will need to be supported and included in the evolution of all such developments.

Analysing the Data

Data analytics reveals trends and metrics that are otherwise lost in a mass of information. If done well, it will provide a clear picture of where you are now, where you’ve been and where you should go in ther future.

Data analytics relies on a variety of data preparation, visualization and business intelligence tools (such as PowerBI and Tableau) for the processing of data and the production and dissemination of reporting dashboards. For the greatest flexibility in data manipulation and deep dive analyses, open-source languages such as Python or R are invaluable.

We focus on 3 kinds of analytics:

  • Descriptive
    Analytics    ‘What happened?’
    Summarising the data in a meaningful way.
  • Diagnostic
    Analytics    ‘Why something happened?’
    The factors and reasons for past performance.
  • Advanced
    Analytics     ‘What might happen?’
    Prediction modelling and other techniques.

In advanced analytics, models are trained using machine learning techniques which can then be used to make predictions. In addition to prediction modelling, other advanced analytics include:

  • Clustering Analyses - forming groupings that share common characteristics, such as customer or user segments
  • Time Series Analyses - identifying trends and cycles over time such as in sales or demand forecasting
  • Textual or Sentiment Analyses - interpreting and classifying qualitative text data into themes to discover, for example, how customers feel about a brand or product.

Enabling Businesses to be More ‘Data-Driven‘

Clear visualisations should be produced that enable all stakeholders to understand the insights and knowledge gained from the analytics. Telling the stories in the data will help stakeholders understand the importance of these insights.

Insights are valuable only if they are understood by relevant stakeholders and then applied to improving business decision-making. They need to be useful, not just interesting. We advise on how to implement the insights generated from the analytics, for example, by focusing customer retention teams on high-priority customers, more efficiently allocating marketing budgets or investing in more appropriate levels of staff to handle future demand forecasts.

Where appropriate, we also highlight areas where actions can be automated, for example in triggering automatic external customer notifications or internal notifications to customer service teams.