Company data is information on the company’s aspects, interests, and inclinations. This is not just internal data like sales stats or customer relationships but also external data about the industry, location, company size, and customer behavior.
Company data primarily comes from internal information from the company itself. This includes customer information, company performance statistics, software, assets, manuals and designs, financial information, and all other data in the company IT Systems or in an acquired company’s databases. Much of this information, however, can come from secondary sources like the data vendors for review on our site.
All Database Management Systems (DBMSs) run by companies consist of one or more system-defined (i.e., built-in) simple data types. Two basic DBMS operators support extra operators for simple data types, including decimal, numeric, float, real, int, samllint, tintyint, char, and varchar.
These datasets also include information about subjects like employee performance reviews to lists of leads, transactions records, target markets, and product catalog data. Finally, many company datasets include information about business processes such as business goals or supply chain operations.
Due to the broad array of information it contains, company data helps you make better business decisions at every level. Additionally, by bringing to light the weak points in your own organization and your competitors, you can make better long- and short-term decisions.
To test the quality of your company data, consider the issues of accessibility, usability, reliability, consistency, and completeness. In other words, ask yourself questions like the following:
How data is gathered and analyzed depends on the intended use and on the nature and size of the organization. Further, data collection must comply with company and industry codes as well FOIA and other Privacy Protection legislation. In this vein (and in the interest of effectiveness and efficiency), only data that can shed light on company issues or opportunities should be collected.
The next step in vetting this data is an internal and external assessment of the company’s organization and goals. After this, the databases themselves can be evaluated for the short term or the long term as resource allocation is different for each.
Absa uses data analytics and data science models, combined with basic customer data and other data sets and variables, to pre-empt customer behavior and identify new customer needs. [Absa’s] ambition is to become a digitally led bank that is centred around the customer.
Agrimoney Data provides more than just agricultural yields; they also provide commodities market data, companies data for industrialists and investors, and farming news and analysis.
Their financial data is real-time and their commodities data highly granular, with several sub-categories for each commodity.
Dell Technologies Digital Transformation Index tracks the rate of digital change among thousands of businesses in twelve industries around the globe. This metadata analysis enables IT and technical benchmarking for any company.
LinkedIn Company Data includes not only data about for-profit and non-profit companies of all sizes, but also the people who work in them, from CEOs to interns. LinkedIn also tracks data on certifications, credentials, and events; they even offer professional training and host events themselves.
In all, this information improves social and professional networking while providing opportunities for employee recruitment and B2B sales.