Data analytics are only as good as the data used to derive them—high data quality results from good data governance policies and sound data management practices. This, in turn, leads to quality insights you can trust.
Why is Data Management for Data Analytics Important?
As organizations digitize their operations, they generate more data, which can contain insights and correlations that only become apparent when analyzed. Data-based decisions are more likely to lead to expected and desired outcomes than those based on a gut feeling. Getting trustworthy data requires an investment in data management. With the right data, decision-making is more effective, increasing customer satisfaction, revenues, and profitability.
Data Management Practices for Analytics
Below are aspects of data management that impact data analytics:
Data Governance
Data governance influences the policies, processes, and roles an organization uses to ensure high data security, integrity, quality, and availability are all performed in a controlled and responsible manner. Data governance is essential to data analytics because it impacts data quality. It catalogs where data is, its quality, and its importance, along with driving data consistency.
Data Storage
How and where data is stored needs careful consideration of latency, capacity, high availability, and flexibility. Performance and latency are greatly improved by using high-speed media with parallel access capabilities. Flexibility refers to storing unstructured data, such as video files, that can be kept on-premises or in the cloud. Storage tiers support the notion of hot, warm, and cold storage with automated data migration to match demand. Storage redundancy is the key to high availability. In case of device failure or disaster, data needs to be protected against loss.
Data repositories can be on-premises or in the cloud. Having the flexibility to host data on different clouds is beneficial because it makes economic sense to gather data into a repository close to where it is created.
Data Security
Operational and other data for analytics needs to be protected from accidental and malicious loss. Data security measures ensure only authorized access and act as a compliance control for sensitive data. Role-based access controls (RBAC) restrict data access so it’s only available to authorized users. Encrypting data at rest and in motion protects against ransomware and snooping attacks.
Data Integration
For effective analytics, data sources and data warehouses need to be connected. The flow and preparation of data should be centrally managed to keep administration costs under control. Data integration technology helps organizations quickly create data pipelines to manage the data flow.
Data Warehouses and Data Marts
Raw data must be organized for effective data analysis to handle decision-support queries. Data is typically ordered, loaded, and indexed into appropriate star or snowflake schemas in a data warehouse or data mart.
Business Intelligence Tools
Reporting and visualization dashboards make it easy for analysts to understand the data and gain new insights. Business intelligence tools make it easy to create dashboards that follow data changes in real-time and illustrate hidden data correlations.
AI and Machine Learning
Artificial intelligence (AI) and machine learning technologies can be applied to automate the mining of large quantities of raw data to look for relationships within a data set. Predictive analytics can forecast potential outcomes to inform decision-making.
Data Management Examples
Different approaches to managing data can be used depending on what questions need to be answered by data analytics. Below are some examples:
Customer Data
Client teams must remain current with the disposition of existing customers to know when they need additional support. Source data from service desk applications such as ServiceNow and Jira track open cases, escalated issues, and enhancement requests. Analytic dashboards provide a 360-degree view of the customer to keep client teams in touch with problems, licenses approaching renewal, and new opportunities that salespeople can explore.
Marketing Data
The inception of a new customer opportunity can start with a web search, resulting in a website visit before a conversation can occur. Marketing can use dashboard data from search engine activity, website visits, and prospecting systems such as Salesforce to track the customer’s journey. Sales teams need near real-time data to interact with prospects while researching solutions, such as visiting the website.
Logistics Data
The movement of goods from suppliers, through delivery companies to customers such as retailers, is often a complex journey that places heavy demands on analytics systems. Shippers need to know where their produce is and its condition. Logistics companies must optimize loads and routes. At the destination, warehouses and retailers must understand when their shipments will arrive. Analytics systems show bottlenecks, highlight missed deliveries, and help with route optimization and delivery time predictions.
IoT Data
Data from Internet of Things (IoT) devices is generated raw from sensors in smart devices such as cameras, temperature probes, and digital locks. Before data is loaded into an analytical system, it must be filtered and downsampled to avoid overloading servers and networks. This initial processing is done in a gateway server close to the edge of a network before being passed to a data pipeline process.
Actian and Data Management for Analytics
The Actian Data Platform transforms your business by simplifying how you connect, manage, and analyze data on-premises and across one or multiple clouds. Actian analytics and transaction systems have hundreds of pre-built data integrations to make data analysis easier. You can start with the Actian Data Platform using a free trial here.