1200
627
310
310
Read it in 6 minutes
Datahub Consulting undertake a number of workshops with clients to understand what they are doing with data, what they want to do in the future, and the challenges that they face. From there we can help businesses create an in-depth achievable data roadmap to map out the journey from current position to where you want to be.
We can offer a free 2 hour workshop for any organization which will be facilitated by one of our architects. Prior to the workshop we will discuss what you would like to get out of the workshop and who we would need to speak to. Alternatively, if you engage in a project with us, as part of the project we will undertake a more in-depth workshop that can take up to 5 days (depending on the size of the organization and complexity of the data)
One of the initial objectives in the workshop is to understand the maturity of the data within the business.
So, what do I mean by data maturity? This is nothing technical, and when I explain it to clients, or presenting at a conference this becomes clear. Data used in analytics goes through four key stages and these stages define the maturity of the data. From a delivery point of view, as the value to the business increases, so does the difficulty to implement. Getting Datahub Consulting to support you through your data journey will make the implementation seamless.
These maturity stages are best described in an analytics maturity curve.
These steps are:
Let’s have a look at these steps and explain their meaning with real world examples.
Is the ability to capture historic data in a data warehouse. When I talk of historic data, I refer to any data related to the past. This could be a year, month, week, or even 30 minutes ago. If you capture real-time data, then 15 minutes ago would be considered historic data.
A data warehouse is a large database either on-premise or in the cloud, but the database structure is optimised for data retrieval. Often referred to as OLAP (Online Analytical processing) with Dimensions and Fact tables. Data warehouses differ from a traditional OLTP databases (Online Transactional Processing) as this type of database is optimised for inserts and updates etc. Often referred to as normalized structure. Thats the most technical bit out of the way!
As the data is just historic it gives the organization hindsight into their data and trends and is used as an informative tool. As the axis on the graph above shows it is relatively easy to implement but also offers the business low value.
With the next level of data maturity, after Descriptive Analytics is the ability to analyse the historic data in the data warehouse to make decisions. This is called Diagnostic Analytics. Examples of this would be to answer,
So, using the historic data in the descriptive analytics, we now can analyse this data to provide key insights. From this we could start to make business decisions.
Most organization get to this point in data maturity and are happy with this. But what if you could do more with it. What if in the above example we could predict the 10% loss in sales before it happens! We can do this with predictive Analytics.
This is the point where the vast majority of business systems are at the current time. But organizations are starting to ask about the next stage, predictive analytics.
Predictive analytics is where the data gets interesting. We are now moving away from historic data being the focus to looking into the future. Predictive Analytics uses variables in the historic data (called features) to train and test an algorithm. With data science you always start off with a problem to answer. For example,
From the algorithm there will be an output that will be a prediction complete with a percentage accuracy. Based on the percentage accurate there will be a number of iterations to improve and refine the algorithm. The accuracy will depend on the quality of the data to train and test the model, also the amount of data available is important. So, as time goes on and more historic data is available the algorithm, the output will also improve.
Prescriptive analytics entails the application of mathematical and computational science that Suggests Decision Options to take advantage of the results of descriptive and predictive analytics.
What does this mean in simple terms, Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen.
Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option.
For example,
A manufacturing company need to reduce the delivery costs. On average they make 150 deliveries per week to stores throughout the UK. Depending on the route, the loading of the truck, and even weather conditions. The prescriptive analytics will make recommendations to reduce the number of deliveries to make the deliveries more efficient. This will not only predict the average cost reduction per delivery but will also make the recommendations to achieve this.
Source: Wikipedia
If you want to understand more about data maturity or taking your analytics to the next stage, then Datahub Consulting can help.
We would be able to talk through any medium / long term goal, understand where you currently are and do a workshop. Then help you create a data and analytics roadmap to support your end goal. Datahub can support you through your analytics journey and provide the technical expertise to make your goals achievable.
For more information about Datahub Consulting, or to understand more about any of our services please contact us. We would be happy to sit down over a coffee and let you know more about our growing business and how Datahub could support you with your data requirements.
We do not employ salespeople; our team are all experienced technical specialists that can talk you through any of our services.
Contact us