Business Insights

In this phase, organisations pay attention to the contribution that data can make to understanding why things are happening. The terms big data and advanced analytics are often used in this phase.

Identify useful business insights and actionable recommendations

Instead of just presenting tables and charts of data, in this phase we take one step further.

By using by statistics, predictive analytics and data mining, significant and actionable business insights can be identified. These insights can then be integrated back into the existing business processes.

Orbit SVG_Data Maturity Model ENG phase2 illust
spacers

Our services

IIn the Business Insights phase, we support our customers with our expertise in setting up data lakes, developing data pipelines and applying advanced data science techniques to gain new insights that support business operations.

Read more

Orbit Icons red_Data Science

Data Science

How can we apply computer science, predictive analytics, statistics and machine learning to mine very large data sets, in order to identify specific future events?
Orbit Icons red_Data Engineering

Data Engineering

How do we develop the interfaces and mechanisms to exchange different types of data between the various data stores in real time, so that they can be analysed by data scientists?
Orbit Icons red_Data Lake A

Data Lake

How do we create a scalable, centralised repository for both structured and unstructured data, for better and faster analysis and decision making?
spacers

Why data science?

Information from administrative processes, often structured data recorded in relational databases, is ideally suited to be analysed by means of Business Intelligence tools.

In order to gain deeper insights, however, organisations are increasingly collecting additional, often unstructured data from both internal and external sources. An example of this is marketing information that maps customer characteristics, structures (online) behaviour and aggregates it into customer profiles. This can also be done with log information from machines, photos, social media messages, etc.

As data collection becomes broader and more complex, the application of specialised data science tools is necessary. Although the development of Business Intelligence software in this field is evolving, the functionality offered is insufficient for many data scientists.

24_Structured and Unstructured data-1
spacers

Extract knowledge and insights from raw data 

Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data.

Data science experts not only do exploratory analysis to discover insights from it, but also use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in order to identify the occurrence of a particular event in the future.

A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

 

Data Science A1
spacers

Data science use cases

IT organisations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimise themselves efficiently.

Data Science is disrupting the way we do business. Here are some examples of how dat science is being applied in various domains like Banking, Retail, Manufacturing, Transport, Healthcare, Ecommerce, etc.

Data Science info
spacers

Data engineering

A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files.

This is where data engineers come in — they develop the data pipelines: interfaces and mechanisms for the exchange of and access to data, often using API's. The data may or may not be transformed, and is often processed in real time (via streaming) instead of in batches.

 

25_Engineer vs Scientist-1
spacers

Data lake

A data lake is a centralised repository that allows storage of structured and unstructured data at any scale. Data can be stored as-is, without having to first structure it.

A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video). It can be established "on premises" (within an organisation's data centers) or "in the cloud".

The ability to harness more data, from more sources, in less time, and the ability to empower users to collaborate and analyse data in different ways, leads to better and faster decision making.

Orbit Images Data Maturity SVG B_Data lake
spacers

Data lake versus data warehouse

Een data warehouse is een database die geoptimaliseerd is om relationele gegevens te analyseren die afkomstig zijn van operationele bedrijfsapplicaties. De structuur van de data, en het schema worden vooraf gedefinieerd om het data warehouse te optimaliseren voor snelle rapportage en analyse.

Een data lake is anders, omdat het relationele gegevens van bedrijfsapplicaties én niet-relationele gegevens van mobiele apps, IoT-apparaten en sociale media combineert.

De gegevens worden in hun oorspronkelijke formaat, zonder een structuur of schema te definiëren, opgeslagen. Men weet immers van tevoren niet welke eventueel toekomstige vragen beantwoord zouden moeten worden.

Veel organisaties zien de voordelen van data lakes, en breiden hun traditionele data warehouse met data lake functionaliteit uit, om door de toepassing van data science nieuwe informatiemodellen te kunnen ontdekken.

Orbit Images Data Maturity SVG B_Data Warehouse vs Data Lake