Data Analytics FAQs

• Better targeting for customer acquisition
• Analyse, interpret and deliver data in meaningful ways
• Drive effective decision-making and improve performance

As a starting point what we find works well is a data discovery workshop to understand the quantity and quality of the data you hold, where it is stored and what important business questions you would like answered.

Secondly, join forces with a data partner. For example, Objective has a team of data scientists who work on outsourced analytics projects, including consultancy, proof of concepts, developing visualisations and Power BI Dashboards. Alternatively, we can add skill sets to your in-house technical team.

Yes, in most cases.

The majority of companies we work with have started their analytics journey with the creation of a dashboard, for example Microsoft Power BI along with some visualisations and correlation plots. This way stakeholders can properly understand the data captured and any gaps that need to be addressed can be easily identified. It also provides a good Business Intelligence foundation, to allow you to move on to predictive analytics and machine learning.

The costs are very much associated with what you want to achieve and therefore how much time is invested.

For example, a simple dashboard, where all data sources are up to date and easily accessible can take will take around five days to create and style. More complex dashboards pulling information from a variety of sources will naturally take longer.

Once your dashboard is complete, training is available. This way team members can learn to use the dashboard effectively and become self-sufficient, enabling them to adjust to the report and visuals.

Broadly speaking we follow six steps; of course, these may vary depending on the business questions asked, and the data available.

Typically, the steps are:

1. Problem Definition
2. Data Exploration
3. Data Preparation
4. Modelling (using AI/ Machine Learning)
5. Validation
6. Implementation and Tracking

More detailed information is explained in our blog

Data analytics is more than merely presenting numbers and figures to management. Data analytics comprises: Business intelligence, Machine Learning & Predictive Analytics It is about exploring and understanding your data and using that knowledge to drive actions. Data analytics reveals the patterns and trends within the data, which might otherwise remain unknown.

Data scientists convert raw data into information that helps guide business decisions.

A data-driven culture, data, time to test and learn and a good idea of what you want to achieve.

Data analytics requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easy-to-understand visual format.

For a data project to succeed, it’s advisable to lead from the top, set great examples and assign a data champion as a go-to person for everything data.

Unfortunately, there is not a simple answer to this question.

Data is the most important resource for any analytics project. Therefore, organisations need to capture as much business, transactional and customer data in a structured manner.

What we find works well is a data discovery workshop to understand the quantity and quality of the data you hold, where it is stored and what important business questions you would like answered.

Following the workshop, we can advise on the efficacy and suitability of the data.

There are four main phases of analytics: descriptive, diagnostic, predictive and prescriptive. The right phase for your organisation depends on the stage of the workflow, and the requirement of data analysis. Typically, a data warehouse stores the data.

A data warehouse is a data management system designed usually by an expert in data warehouse services provision to provide meaningful analytics through accurate storage and categorisation of data that can be analysed, quickly and accurately. Over time data coming in from various sources builds a historical picture to enable the user to gain useful insights to inform policy and decision making.

For example, buying trends data pouring in from customers can inform what levels of product inventory are ordered and stocked. In production, information from sensors attached to machines can build a picture of how much down time has elapsed to help schedule workloads and much more.