By Ananth Chakravarthy, RVP Sales, Denodo India
In today’s hyper-competitive business landscape, particularly within the digital sphere, standing out from the crowd necessitates a unique approach. The pathway to differentiation lies in the effective analysis of data, which enables organisations to gain a deeper understanding of their customers and adapt their business strategies for rapid success.
This article explores three vital phases of the data analysis process: the management of raw data, the management of information, and the management of knowledge. By mastering each of these three phases, organisations can maximise their chances of success. Organisations that can systematically leverage their data for analysis consistently outperform those that cannot.
Managing Raw Data
Any data that seems relevant and intriguing for the business qualifies as “raw data.” Raw data could be a sales report for a newly launched product or mentions of a product across social networks, forums, or web reviews.
In recent years, organisations attempted to store raw data predominantly within a data warehouse. However, this strategy is no longer optimal as it fails to consider external sources such as forums, social media, or public relations materials, and it restricts organisations to structured, internal resources.
Raw data exists in a wide variety of different locations, including an organisation’s operational systems like customer relationship management (CRM) or enterprise resource planning (ERP) systems, big data repositories, filled with unstructured data such as social media feeds, and open data sources. Consequently, organisations need to be able to identify and establish connections with this data, regardless of where it may be located.
Managing Information
With reliable connections to widespread raw data, organisations can begin the next phase of the data analysis journey. In the information management phase, organisations separate valuable insights from irrelevant data to establish a repository of key information that profoundly impacts your business. However, it’s crucial not to rush into making analytical decisions at this point, because the process is not yet complete.
In this phase, organisations need to enrich the data, contextualising, categorising, calculating, correcting, and simplifying it. In this phase, raw data is transformed into actionable information. For example, organisations can organise sales reports by region and dissect social network comments by sentiment, such as “neutral,” “positive,” and “negative,” and then they can further categorise this information by region.
Managing Knowledge
The final phase in the data analysis journey is knowledge, which imparts meaning to the gathered information. This stage involves more intricate tasks, such as comparing elements and identifying connections and patterns between them. In this phase, organisations prepare the information that will empower stakeholders to make informed decisions.
For example, in this phase, organisations can assess the performance of all regions and amalgamate sales data with local social network comments from users. At this juncture, organisations can pinpoint crucial issues, such as an abundance of negative comments in California or an unusually low number of comments in Florida.
Consequently, when this information is made available to decision-makers, they might uncover the presence of a local competitor in California, prompting the need for a tailored strategy, and they might realise that not enough marketing efforts were directed towards Florida, where many potential customers remain unaware of the organisation’s product line.
Logical Data Management: A Tripartite Approach to Success
To excel in each of these three phases of data analysis, organisations require a platform capable of extracting knowledge from raw data, and this is where a logical data management platform plays a pivotal role.
Logical data management platforms overcome the limitations of traditional data management platforms, which rely on the physical replication of data from multiple source systems into a single repository, such as a data warehouse or a data lake. In contrast, logical data management platforms connect to data logically rather than collect data. This is made possible by data virtualisation, a modern data integration technology.
Data virtualisation streamlines the data analysis process into three simple steps: connecting, combining, and publishing. Data virtualisation establishes a data abstraction layer that enables connections to disparate data sources. This enables organisations to quickly gather views of the data; filter it; create a unified view that encompasses only the data that is relevant to your business (i.e., information); and enhance it by having it be automatically transformed into actionable knowledge, on the fly. Data virtualisation then empowers organisations to quickly furnish this information to decision-makers so they can effectively drive the business forward.