Explaining analytics to people outside of the industry is a test. There is no assumed knowledge and no hiding behind TLAs (three letter acronyms). Most of the time we fall back on explaining why implementing analytics offers much more than number-crunching in Excel. Whilst Excel absolutely has its place, you are typically presented with a section of raw data all at once, in the form of a spreadsheet, which can be difficult to interpret, put in order of importance, and determine what is and isn’t outdated at a glance. In the world of analytics, where data sets can be vast, there are more efficient and effective ways of determining and sharing insight.
Data visualisation or analytics tools provide a flexible and reliable way to identify the pertinent information and present it to a target audience in a manner they can easily understand. By using visual representations of your data via dashboards, business users can quickly interrogate the data and drill up and down levels to find the relevant insight they require, without having to understand the tools they are using.
How to implement analytics within your organisation
Once a requirement for analytics is identified within an organisation, there needs to be a way to implement it. Good practice is to begin by looking for ways to use hard facts and information to determine future business decisions. This is called having a data driven strategy. Next, you must determine which metrics and analysis techniques are to be used for collecting those facts and information; for example, what is it that you are going to measure and are you going to look at past data or try to predict future events?
Once you have the answer, you must then collect the relevant data. This may be done internally – from ERP systems – or externally, from sources such as social media, or other publicly available data. The final step is to ensure the data is in the correct format, so that the analytics tool is able to analyse it in the most efficient way possible. You can now analyse and report on your findings using the analytical tool you have selected.
The components of an analytics solution are varied and depend on who it is you are talking to. Needless to say, the types of solutions differ considerably. Some are as simple as a desktop analytical tool running over a simple spreadsheet or database. Others are highly bespoke using numerical and non-numerical data to create insight. Data can be drawn from your organisational systems or from third party platforms (subject to the right permissions), then used to create data warehouses or data marts. Unlike databases that are designed around being transactional (input and output), data warehouses and data marts are designed especially for running analytics.
Data from different sources is easier to analyse when it has been converted into the same format, before being loaded into a data warehouse – this would require the use of an ETL (extract, transform and load) tool. Your data may contain duplicates or missing data, in which case you would need some form of data cleansing solution. You may also want to bring data feeds from other internal or external data sources in to your organisation which will require special integration software to help with this. Once you have your data in the format required, you can then set your analytical tool to report on the discoveries it makes. The complexity of a solution depends entirely on the business problem you are aiming to resolve. The analytical tool you use only forms a small part of the whole solution. The majority of market-leading analytics visualisation tools have both cloud and on-premise deployment offerings that will access the data to be analysed no matter where its location, giving the customer the choice.
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source http://news.statii.co.uk/analytics-going-back-to-basics/
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