Data Analytics & Business Intelligence
While data is the core of every modern business, many companies don’t necessarily make the best use of it. Our goal is to help organisations of all sizes to gain a better understanding of their data so that they can make well-informed decisions. This requires the right processes and tools. Our team of experienced professionals has the expertise and knowledge to help you maximise the potential of your data. We provide a wide range of services to help you manage and analyse your data, including data strategy, data warehousing and business intelligence.
Advice for your data strategy
Working with data requires a clear data strategy. This is an important aspect for any successful business intending to take a more data-driven approach. The data strategy outlines a plan for how an organisation can collect, store, manage and use data to support its goals and objectives. If you have a data strategy, you can also ensure that your company has all the information it needs.
What are the features of an effective data strategy?
By taking the time to develop a detailed and comprehensive data strategy, your company can get the competitive edge. Based on your strategy, you can use data to make well-considered business decisions. This can produce greater efficiency, improved decision-making processes and, ultimately, better business results.
The components of an effective data strategy include the following:
identifying what types of data are important to the organisation and how they will be collected;
a plan for how data is stored and protected;
setting up processes for analysing and interpreting data;
implementing systems and tools so that data can be converted into useful insights.
Whether you have a small or large business, our specialists can help you to efficiently devise a data strategy for your organisation.
We can train your organisation to become data literate
Part of an effective data strategy is data literacy – understanding and being able to interpret the data properly. Being data literate means being able to use data to make informed decisions, and to understand the context and limitations of the data. It also means being able to evaluate data critically, avoid common pitfalls and biases, and communicate data-driven insights effectively to the various target audiences. Without these skills, data is generally unusable.
Baker Tilly has a detailed plan for training your organisation in data literacy. As part of this training, your employees will learn a range of skills, including the following:
understanding data concepts and terminology;
how to analyse data using tools and techniques, like spreadsheets and statistical software;
how to communicate data effectively through images, reports or presentations;
how to collect and organise data to ensure that it is of a high standard;
awareness of concerns about data privacy and security;
how to use data to support decision-making and problem-solving processes.
If you would like more information about this, please feel free to contact us.
Data analytics: from retrospective to predictive analysis
The field of data analysis consists of a hierarchy that ranges from relatively simple to complex analysis. In-depth analyses and predictions are possible, depending on the quality of the data collection. It is important to point out that this journey may be a gradual process, which involves you adding new techniques and tools over time. Each step has to be implemented with a clear business goal in mind, and results have to be monitored and evaluated on a regular basis.
In recent years, our team of consultants and data scientists has helped several clients on this journey from simple to complex analysis. The necessary steps are outlined below:
Data collection and preparation
Analysis starts with identifying the types of data that are important to your business, and developing a plan for collecting and preparing that data. This could include collecting data from various sources, such as customer databases, social media and sensor data. Data has to be cleaned, converted and formatted so that it can be used for analysis.
Descriptive analyses
Once the data has been prepared, descriptive analysis can be used to gain a general understanding of the data. This could include simple statistical analysis, such as calculating averages and standard deviations, as well as visualisation techniques, such as creating charts and graphs. This can help to identify patterns and trends in data.
Diagnostic analyses
The next step in the process involves using the calculations and data from the descriptive analyses. Diagnostic analysis gives organisations better insight into the underlying causes of the patterns and trends identified by descriptive analytics. This kind of analysis uses techniques such as regression analysis and drill-down analysis, which can help to identify the root of the problems as well as opportunities.
Predictive analyses
Predictions can also be made once one has more complex types of data. Predictive analyses are used to make forecasts about future events or outcomes based on historical data. Techniques such as machine learning and statistical modelling are used for this. These techniques can assist companies when it comes to making strategic decisions and planning for the future.
Cognitive analyses
Finally, cognitive analyses produce the most powerful insights to be gained from data analysis and are used to gain more in-depth insights into the data. They include techniques like natural language processing, sentiment analysis, and image and video analysis. These kinds of techniques can help companies extract meaning from unstructured data and get a better understanding of customer sentiment and behaviour.
