In the contemporary and highly competitive business environment, especially in the digital realm, distinguishing oneself requires an innovative strategy. The key to setting oneself apart lies in the thorough examination of data, allowing organizations to develop a profound understanding of their customers and adjust their business strategies for swift success.
This article delves into three crucial stages of the data analysis process: handling raw data, managing information, and overseeing knowledge. Proficiency in each of these stages empowers organizations to optimize their prospects for success. Companies that adeptly utilize their data for analysis consistently surpass those that do not.
Managing Raw Data
Any data perceived as pertinent and intriguing for the business is categorized as ‘raw data.’ This could encompass a sales report for a recently launched product or mentions of a product across various social networks, forums, or web reviews.
Historically, organizations endeavored to centralize raw data primarily within a data warehouse. However, this approach is no longer optimal, as it neglects external sources like forums, social media, or public relations materials. Moreover, it confines organizations to structured, internal resources.Raw data is dispersed across various locations, spanning an organization’s operational systems such as customer relationship management (CRM) or enterprise resource planning (ERP) systems, expansive big data repositories containing unstructured data like social media feeds, and open data sources. Consequently, organizations must be adept at recognizing and establishing connections with this data, regardless of its diverse locations.
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With reliable connections to extensive raw data, organizations embark on the subsequent stage of the data analysis journey. During the information management phase, organizations sift through the data, extracting valuable insights and discarding irrelevant information to establish a repository of key data that significantly influences their business. However, it is imperative not to hastily make analytical decisions at this juncture, as the process is not yet complete.
In this phase, organizations focus on enhancing the data by providing context, categorizing, calculating, correcting, and simplifying it. Raw data undergoes transformation into actionable information. For instance, organizations may organize sales reports by region, analyze social network comments based on sentiment (such as ‘neutral,’ ‘positive,’ and ‘negative’), and subsequently further categorize this information by region.
The concluding stage in the data analysis journey is knowledge, which bestows meaning upon the collected information. This phase entails more intricate tasks, such as comparing elements and identifying connections and patterns among them. Organizations, in this phase, refine the information to empower stakeholders in making well-informed decisions.
For instance, during this stage, organizations can evaluate the performance of all regions and integrate sales data with local social network comments from users. At this point, organizations can discern significant issues, such as an excess of negative comments in California or an unusually low number of comments in Florida.
As a result, when decision-makers access this information, they may uncover the existence of a local competitor in California, prompting the necessity for a tailored strategy. Simultaneously, they might realize that insufficient marketing efforts were directed towards Florida, where numerous potential customers remain unaware of the organization’s product line.
Logical Data Management: A Tripartite Approach to Success
To excel in each of the three phases of data analysis, organizations require a platform capable of deriving knowledge from raw data, and this is where a logical data management platform plays a pivotal role.
Logical data management platforms overcome the constraints of traditional data management platforms, which rely on physically replicating data from various source systems into a centralized repository, such as a data warehouse or a data lake. In contrast, logical data management platforms establish connections to data logically instead of accumulating data physically. This is facilitated by data virtualization, a contemporary data integration technology.
Data virtualization simplifies the data analysis process into three straightforward steps: connecting, combining, and publishing. By establishing a data abstraction layer, data virtualization enables connections to diverse data sources. This allows organizations to swiftly gather perspectives of the data, filter it, create a unified view that includes only the pertinent data for the business (i.e., information), and enhance it by automatically transforming it into actionable knowledge on the fly. Subsequently, data virtualization empowers organizations to promptly provide this information to decision-makers, enabling them to effectively propel the business forward.
Navigating the hyper-competitive business landscape demands a strategic approach anchored in data analysis. Organizations must adeptly handle raw data, refine it into valuable information, and ultimately derive actionable knowledge. Recognizing the significance of these phases, it becomes evident that a logical data management platform is pivotal. Unlike traditional methods, such platforms leverage data virtualization, connecting logically rather than physically. This innovation streamlines the analysis process, facilitating rapid connections, integration, and publication of relevant insights. The power to transform raw data into actionable knowledge positions organizations for success. In essence, the ability to identify patterns, establish connections, and derive meaningful insights empowers decision-makers to make informed choices, enabling businesses to not only stand out but also thrive in the dynamic digital landscape.