Sources of Data and the Need for Selectivity
Data, often called 'the new oil,' can be structured, semi-structured, or unstructured and can be extracted from assets, processes, customers, employees, workplaces, warehouses, and a host of external sources. Hoarding data without reason not only increases costs of storage, processing and analysis but also increases the chances of confusion, misinterpretation and misrepresentation of irrelevant data and outdated insights. Hence, organizations must make an effort towards understanding the data they truly need.
Visit our Data Analytics Page
For instance, an organization in the manufacturing space could extract valuable insights from the data generated by plants and industrial assets. However, data from social media or the internet may not be of much use to it unless it is related to weather forecasts or policy updates from the government and related websites. The bottom line is that each organization has to decide not just what data adds value and what does not but also how to store such data and for how long.
Structured data may be stored in data warehouses, while raw data is usually stored in data lakes that support multiple data types. Data storage is possible on the cloud, on-site, in hybrid models, and in Hadoop, MongoDB, RainStor, NoSQL databases, Object storage, etc.
Data security is paramount, and classification, encryption, firewalls, authentication methods, access management, and various other measures need to be in place to ensure that sensitive data is protected at all costs.
Mining, Analyzing, and Visualizing Data
The tools that organizations may use for data mining include Rapid Miner, SPSS Modeler, KNIME, and Orange. These tools help to extract the required data from warehouses. Organizations can harness tools such as Apache Hadoop, Apache Spark, SQL, Presto, Splunk Hunk, and others to analyze data and uncover insights.
Visualization and communication of these insights to end users constitute the next step. Tools that can be used for visualization include Tableau, PowerBI, Plotly, Python, and Qlik Sense.