In my previous article, I discussed about the mammoth amount of IoT data generated to service providers and the adoption of data analytic to maximise the potential of turning data into true enterprise assets through the adoption of Data Science (analytics, machine learning, predictive algorithms etc). However, organisations do not just have IoT data, organisations have different types of data from different sources such as manual input by users, export to or imported from other systems. Data are created and stored at different periods and in different databases using different generations of technology. Different databases support different business applications also developed over different periods of time. As Steve Breamer – VP Continuous Improvement Transformation Systems, BP rightly said: “Everything is related, nothing is connected”.
In this article, I want to focus more on how to develop a conceptual model which is technology, product and tool agnostic to define how we could connect different sources of data together logically first before implementing data analytics. Let’s think conceptually before diving into the “Data Lake” and other fancy name technologies:
The benefits of connecting everything which is related are that organisations can then have a holistic view (aka: connecting the dots) of what’s going on in the whole organisation. I am discussing below how multiple areas could trigger each other and their dependency / interdependency on each other:
Data:
Customers
Purpose:
The observation &calculation of customer spending on what products what services
Impact:
•To more effectively identify customers’ needs and to increase profits
•Can also be used to check against Customer’s Satisfaction Survey, Customer Service Desk, R&D Innovation, product & services
Data:
Billing
Purpose:
The exchange of invoices against payments
Impact:
•To more effectively check incomes against expenditure
•Can also be used to check against Customer’s Satisfaction Survey and Customer Service Desk
Data:
Finance
Purpose:
Income against expenditure
Impact:
•To more effectively plan financial and budget forecast
• Can also be used to check against Billing, Supply chain, Employees
Data:
Products & Services
Purpose:
The offering to customers
Impact:
•To more effectively check the supply against demand of products and services
•Can also be used to check against Customers and supply chain
Data:
Business Development
Purpose:
Cultivation of new markets and new business
Impact:
•To more effectively target potential emerging markets and new businesses
•Can also be used to check against R&D Innovation, Customers, Customer Satisfaction Surveys
Data:
R&D Innovation
Purpose:
Development of new products and services
Impact:
•To more effectively attract new markets and new businesses
•Can also be used to check against Customers, Business Development
Data:
Employees
Purpose:
Workforce planning
Impact:
•Decisions on utilisation of resources
•Decision on training, recruitment, retention
•Can also be used to check against Customer Satisfaction Survey and Incidents Log
Data:
Customer Service Desk
Purpose:
Workforce Planning
Performance measure
Impact:
•Decisions on utilisation of resources
•Decision on training, recruitment, retention
•Can also be used to check against Customer Satisfaction Survey and Incidents Log
Data:
Service Desk Incidents log
Purpose:
Performance measures
Impact:
•Quality Control and to improvement customer services
•Can also be used to check against Customer Service Desk and Customer Satisfaction Survey
Data:
Customer Satisfaction Survey
Purpose:
Performance measures
Impact:
•To listen to the voice of the customers and to improvement customer services.
•Can also be used to check against Customers, Products & Services and Customer Service Desk and Incidents Log
Data:
News Channels
Purpose:
PESTLE factors (political, economic, social, technological, legal, environmental)
Impact:
•Decisions on business strategies in response to external PESTLE factors
•Can also be used to check against Supply Chain, Customers, Business Development
Data:
Supply Chain
Purpose:
Seeking Products and Services from external sources
Impact:
•To more effectively determine the demand of products and services
•Can also be used to check against Customers, Products & Services
The incompatibility of different databases and systems is still posing a big challenge to organisations when it comes to a single view of organisational analytics as the current practice of analytic tools just give the business the analytics of a certain business area at a time based on the database it has connected to. Some analytic functions are embedded in an application and can only produce analytics for this particular function. Business users still need to manually join different analytic snapshots in order to react to the situations and to make decisions. This has also proved to be not cost and time effective. Data analytics should start from a logical level.
A standard corporate data model for both structured and unstructured data is to be created to map to multiple systems. Literally, for example, we are talking about doing 12-way translations across the 12 systems illustrated in the above diagram. A corporate data model (CDM) is the master, each of these 12 data models has a mapping table to the CDM and these 12 data models do not need to understand each other as long as they all map to the CDM. When a data analytics tool is implemented, it only needs to be configured to understand one single data model ie the CDM and can understand and absorb multiple data from different data sources via the CDM. This is the way to achieve a holistic approach of organisational wide analytics. Once we have a logical data analytics conceptual model, we can then choose the appropriate tool. The above example only provides an idea of data in typical organisations. There are organisations with far more complex data structure and it is more important to develop a logical data analytics conceptual model to enable the analytics more manageable.
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Disclaimers: This article was written entirely based on my personal opinions. It has nothing to do with either my current or previous organisations I am / was employed by.
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