TOP OF THE LINE SECURITY
Data Eco’s data and analytics service is run on the Exasol data platform, meaning that customers are benefiting from the use of the fastest and most intelligent analytics database. By partnering with such a highly regarded industry leader, Data Eco is providing our customers with top of the line security and customer service.
Exasol is a top-quality provider of real-time data feeds and integration services that allow your firm to stay on top of the financial markets and adjust to media based on social data trends. By utilising the power of Exasol’s advanced machine learning and bench marking, Data Eco can help any company learn how to be successful based on past performance and industry trends.
THE NEED TO TRANSFORM DATA AND WHY?
The Businesses that we helped over the years collected and processed data integration and stored it in a large database. Normally this was referred to as a data warehouse. This allowed the organisations to transform and model the data to allow it to give some business meaning. The OLAP (On-line data Analytical Processing) conventions over the years have varied slightly but were mainly following the same principles. They would simply transform the data into what was a de-normalised format of Facts and Dimensions data.
With the data being stored in a dimensional state the ability to see analysis of the factual data from many different view points was made available. With having the data modelled this way, we could easily aggregate totals of factual data up to the highest dimension. In most companies, the Product or service was commonly chosen as dimension and this aided the holistic view of measures such as sales by Product Category. The obvious key decisions were normally around which products to sell and which to stop.
THE INITIAL ANALYSIS
In the early 90’s, the technology was very specific and we use to work on systems that created OLAP cubes to aid business analysis. The cubes which were initially just 3 or 4 dimensions grew to 8 or 9. I even worked on a 10 dimensional cube for Burger King in their Sales Analysis of restaurants data. The process of populating the cubes was expensive in them day’s and often analyst would have to wait days for the cube to be built to get their reports written.
Even though the technology was basic it still showed that the business questions needed to be framed in a particular way so they could get the most data value from the OLAP cubes. The initial design steps would always include as many different types of business questions that were needed to be answered? The design tips of using the 5 W’s approach was always a good way to facilitate extracting the requirements from the companies.
By asking the What, Where, When, Who and Why always gave us enough ideas of what data facts and dimensions were needed to answer the questions. We still use this today and is an effective step in ensuring we model the data well. This is also true for the search engines on the world, where you can now see how google includes the where and why factor of a keyword question!
DEFINING THE DATA MODELLING LAYERS
When it comes to Data Modelling, the way we establish dimensional relationships and fact summaries allows for the most use of the data collected. The data is brought on to the platform using the integration processing and lands the data in the staging tables. This data is then joined and transformed to allow the best analysis to meet the many business questions.
To look at where the most sales are? When did they take place? or Who were the buyers and have they bought before?
By having the factual data aligned to the dimensional data we can answer the sales fact by product , by date , by Supplier, by Buyer, etc
