Top Ten Terms to Know for Enterprise Information Management and Analytics

At a time when data and analytics are critical to managing operational and economic uncertainties, many organizations still struggle to effectively leverage their data for required insights.

New tools and technologies are developed and released at an unrelenting pace, and the number of terms, acronyms, and buzzwords to assimilate are daunting. Ten terms related to Enterprise Information Management (EIM) are defined below, along with additional details you should be aware of to assist in your journey to become data-driven.

1. Big data: Big data has been commonly characterized by the three V’s – Volume, Velocity, and Variety. Due to the number of sources that include traditional enterprise systems, websites, mobile devices, and sensors, data is being created in larger Volumes, at increased Velocities in a Variety of formats. The challenge is in solving the complexities of big data to gain transformative insights for your organization. For example, digital marketers use artificial intelligence to make sense of large data sets, such as social media posts. This enables them to understand consumer sentiment and how different segments will behave in the future so they can adapt their targeted campaigns accordingly.

2. Database: Databases enable information about your organization to be stored, managed, and accessed by multiple users and applications for various purposes. IT executives have many options to choose from when selecting a database. When doing so, it is important to understand the differences between SQL-based and NoSQL-based databases. Relational Database Management Systems (RDBMSs), which are built and maintained with Structured Query language (SQL), have been the traditional databases used by most organizations. More recently, NoSQL databases have become popular for their ability to handle large volumes of unstructured data for analytics purposes with little to no data preparation required.

3. Data warehouse: A data warehouse has traditionally been defined as a repository of an organization’s data specifically structured for query and analysis. Current cloud warehouse offerings support additional use cases and data structures, including relational and non-relational data. Additional benefits include storage and compute scalability, which is available on a pay-as-you-go basis. Integration with other tools such as machine learning and business intelligence applications ensures you get the most out of your data by building new insights and presenting them in compelling dashboards.

4. Data lake: A data lake is a powerful solution that enables organizations to store data from a variety of sources in their native state. This means that you can import multimedia files, sensor data, structured files, binary data, social media, and a variety of others without defining and enforcing data structures. Organizations leverage this capability to store data in one centralized location, providing users a single place to look for all sources of data. Data lakes are also an ideal repository for data discovery and data science initiatives.   SQL queries, full-text search, real-time analytics, and machine learning can be applied within a data lake to gain new insights.

5. The cloud: The cloud  or “cloud computing” is the on-demand availability of computing system resources through data centers that are actively managed by cloud vendors. Cloud computing allows organizations to benefit from various technologies without maintaining deep knowledge or expertise in-house. The support and maintenance of the technical infrastructure are outsourced, resulting in significant cost and time savings. A few of the key benefits  include quick ramp-up times, first-class security, scalability, cost and performance predictability, and the variety of solutions offered.  

6. Metadata: Metadata is a set of data that provides information about other data, or “data about data.” Metadata can be generated manually or automatically and is an important aspect when managing data quality. When data is coming from multiple sources, data must be described correctly to ensure data accuracy and to minimize risks to analytic models, reports, and dashboards. Organizations that are proficient with metadata management engage data stewards who have functional expertise. Metadata management tools enable this capability by presenting details in the appropriate context for business users while ensuring role-based access.

7. Data cleansing and Data enrichment: Data cleansing is the process of finding and correcting inaccurate data. Maintaining accurate client data for outreach and marketing purposes is critical. There are a variety of third-party services available to correct common problems. For example, deduplication services identify groupings of duplicate records maintained in your systems that impede your ability to create a single view of the customer. Address, phone, and email verification services help minimize time and resources spent reaching out to invalid contacts.
   
   Data enrichment is the process of merging third-party data from an authoritative source  to enhance or improve raw data. For example, demographic data enrichment adds details like marital status, household income, and credit information. This can help you identify common attributes of your best customers that will assist with future marketing campaigns and promotions.

8. Data governance: Data governance is the organizational approach to data and information management, formalized as policies and procedures that encompass the full life cycle of data, including acquisition, development, use, and disposal. An effective data governance program aligns departmental goals to strategic objectives and mobilizes stakeholders across organizational functions to address data priorities.

9. Analytics: Analytics  is the use of data and computational analysis to answer business questions, discover relationships, predict unknown outcomes, and automate decisions. At its most simplistic, there is descriptive and diagnostic analytics. “What happened, and why?” These are retrospective in nature and comprise traditional analytics, using an organization’s existing information. Advanced analytics addresses “what will happen” (Predictive Analytics) and “how can we make it happen” by arriving at a decision (Prescriptive Analytics).
10. Semantic layer: A semantic layer draws complex data into business terms for a digestible view across the organization. The semantic layer sits between the database (e.g., Microsoft SQL Server) and a reporting tool (e.g., Microsoft  Power BI). A semantic layer can be deployed in two ways – as a multi-dimensional model or a tabular model. Within SQL Server Analysis Services (SSAS) or Azure Analysis Services (AAS), there are a couple of ways to deploy each model, but the primary methods used are MOLAP for multi-dimensional models and cached mode for tabular models. Each method has its pros and cons in terms of performance and resources used. Selecting between the two is contingent on the underlying data structures and the reporting objectives of the business.

Analytics should accelerate your business, not slow it down!

Enterprise Information Management and analytics are as crucial as ever, and organizations continue to face difficult decisions determining their data and analytics priorities. Leveraging a trusted advisor whose job is to stay up to date with the newest technologies and best practices can be hugely beneficial for your organization. If you are looking for a partner on your analytics journey, consider a partner with CCG’s expertise and experience. Find out how you can accelerate your strategic efforts and make the most of your data by clicking here  or emailing info@ccganalytics.com.

Written by CCG, an organization in Tampa, Florida, that helps companies become more insights-driven, solve complex challenges and accelerate growth through industry-specific data and analytics solutions.