Ask an Architect: 5 Steps to an Effective Salesforce Data Management Strategy (Part 1) (2023)

FAQs

What are the steps of data management? ›

The data management process includes a wide range of tasks and procedures, such as: Collecting, processing, validating, and storing data. Integrating different types of data from disparate sources, including structured and unstructured data. Ensuring high data availability and disaster recovery.

MDM stands for Master Data Management. To put it simply, it means creating a single source of truth, or one master “golden record” for all versions of a record across all systems. This could be a person, an object, or place, and a CRM like Salesforce can be one point of entry for this data.

Provide high availability and disaster recovery. Use data in a growing variety of apps, analytics, and algorithms. Ensure data privacy and security.

The stages through which information passes, typically characterized as creation or collection, processing, dissemination, use, storage, and disposition, to include destruction and deletion.

Definitions: Data Architecture & Data Strategy

Data Architecture defines how data is acquired, stored, processed, distributed, and consumed. On the other hand, the term Data Strategy implies the overall vision and underlying framework of an organization's data-centric capabilities and activities.

They are volume, velocity, variety, veracity and value.

for Salesforce™ Scalable 360° View Automation. Data Quality Protection.

Why do you need Master Data Management? ›

MDM provides a unified view of critical business data with a single master dataset and ensuring consistency of data used in analytical and operational processes. It provides up-to-date data for businesses. MDM collects, transforms, and corrects data and creates a golden record for businesses.

An effective data strategy improves data security by implementing measures to limit unauthorized data access. You can consider all data governance rules and regulations while defining policies and processes.

Only considering internal, traditional data: Today, data comes in many forms and from many sources. A good data strategy should consider all avenues for accessing data, including options like photo data and video data, and external sources such as social media platforms and big data brokers.

Data management in Salesforce deals with Import/Export of data or records to/from a Salesforce organization.

While there is no industry standard for enterprise DLM, most experts agree that the data lifecycle includes these six stages: creation, storage, use, sharing, archiving, and destruction.

The constant cycling of data generation, analysis, integration, storage, and elimination gives Executives the quality data they need to make decisions.

Information life cycle management is the consistent management of information from creation to final disposition. It is comprised of strategy, process, and technology to effectively manage information which, when combined, drives improved control over information in the enterprise.

The data life cycle is the sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival and/or deletion at the end of its useful life.

Sensory memory is the first stage of Information Processing Theory. It refers to what we are experiencing through our senses at any given moment. This includes what we can see, hear, touch, taste and smell. Sight and hearing are generally thought to be the two most important ones.

By definition, data architecture is a blanket term that covers both the policies, rules, standards, and models that govern data collection and how data is stored, managed, processed, and used within the organization.

What is a data architect responsibilities? ›

A data architect is an IT professional responsible for defining the policies, procedures, models and technologies to be used in collecting, organizing, storing and accessing company information. The position is often confused with a database architect and data engineer.

Data Architecture is a framework built to transfer data from one location to another, efficiently. It is full of models and rules that govern what data is to be collected. It also controls how the collected data should be stored, arranged, integrated and put to use in data systems of an organisation.

The 5 V's of big data (velocity, volume, value, variety and veracity) are the five main and innate characteristics of big data. Knowing the 5 V's allows data scientists to derive more value from their data while also allowing the scientists' organization to become more customer-centric.

4) Veracity

It refers to the assurance of quality/integrity/credibility/accuracy of the data. Since the data is collected from multiple sources, we need to check the data for accuracy before using it for business insights.

A comprehensive DMP clearly articulates the roles and responsibilities of every named individual and organization associated with the project. Roles may include data collection, data entry, QA/QC, metadata creation and management, backup, data preparation and submission to an archive, and systems administration.

Data first organizations prioritize business innovation and risk models built around multiple sources of digital intelligence.

Specifically, there are four major pillars to keep in mind for good data management: Strategy and Governance, Standards, Integration, and Quality. Most importantly, in order to be data-driven, an organization must embrace data as a corporate asset.

Transactional data, in the context of data management, is the information recorded from transactions. A transaction, in this context, is a sequence of information exchange and related work (such as database updating) that is treated as a unit for the purposes of satisfying a request.

Data Governance and Master Data Management

Master Data Management includes processes from the creation of master data through to its disposal. Data Governance creates the rules and adjudication of the operational processes that are executed within those processes.

How do I create a thin table in Salesforce? ›

You need to contact Salesforce Customer Support for skinny table. You can't create, access, or modify skinny tables yourself. This table shows an Account view, a corresponding database table, and a skinny table that would speed up Account queries.

There are four master data management (MDM) implementation styles, and their different characteristics suit different organizational needs. These include consolidation, registry, centralized and, ultimately, coexistence.

Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.

The most commonly found categories of master data are parties (individuals and organisations, and their roles, such as customers, suppliers, employees), products, financial structures (such as ledgers and cost centers) and locational concepts.

Most prominently the Data Owner and the Data Steward. Probably several people would be allocated to each role, each person responsible for a subset of Master Data (e.g. one data owner for employee master data, another for customer master data).

Data management includes storage, data security, data sharing, data governance, data architecture, database management, and records management.

Data architecture is a framework for how IT infrastructure supports your data strategy. The goal of any data architecture is to show the company's infrastructure how data is acquired, transported, stored, queried, and secured. A data architecture is the foundation of any data strategy.

There are five core components of a data strategy that work together as building blocks to comprehensively support data management across an organization: identify, store, provision, integrate and govern.

How do you develop a data strategy here Re 6 simple steps that will help? ›

Strategic analysis (sometimes referred to as a strategic market analysis) is the process of gathering data that helps a company's leaders decide on priorities and goals, shaping (or shifting) a long-term strategy for the business.

Salesforce Data Model is essentially the manner in which tables of data are represented within your Salesforce database to make them understandable to anyone who views them. Data modeling helps you make sense of the data residing within your system.

The standard Salesforce Data Loader is the tool of choice for most, business users and consultants alike. It's powerful, convenient and well documented. This tool is best used for repetitive data loads by business users or data migration projects.

The Salesforce Database creates records for leads, tasks, opportunities, accounts, and notes. This is where the actual data is stored. There are a variety of record types that allow linking different business processes to users, customers, and admins based on their user profiles.

Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization.

Students will apply methods for organizing and analysing large amounts of information; solve problems involving probability and statistics; and carry out a culminating investigation that integrates statistical concepts and skills.

Data management is the practice of collecting, organizing, protecting, and storing an organization's data so it can be analyzed for business decisions. As organizations create and consume data at unprecedented rates, data management solutions become essential for making sense of the vast quantities of data.

Data management in Salesforce deals with Import/Export of data or records to/from a Salesforce organization.

What is a data strategy plan? ›

What is a data strategy? A data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage an organization's information assets. All types of businesses collect large amounts of raw data today.

This course examines the core concepts of Data Management, including: definitions, why Data Management is essential to organizational success, the value of data as a whole, key Data Management practices, and more. Data Management is the overarching umbrella around pretty much everything we do with data.

Data management is important because the data your organization creates is a very valuable resource. The last thing you want to do is spend time and resources collecting data and business intelligence, only to lose or misplace that information.

This course comprises four major parts: Counting Techniques and Probability, Organization of Data for Analysis, Statistics, and Integration & Application of Data Management Techniques. Through classroom instruction, assignments and a culminating project, students will study the mathematics required for data management.

Four types of database management systems

hierarchical database systems. network database systems. object-oriented database systems.