For several decades, IT departments in organizations have served as gatekeepers to data. Multiple business units such as Sales and Marketing that make key business decisions have had to go through IT personnel to access the data that they require to make data-driven decisions, a process that consumes time. As some organizations seek better means to be more productive, others out there still believe that data should be handled in such a fashion.
Organizations that seek to maintain a competitive edge in the marketplace must harness insights from data as a best practice for identifying areas that have the potential to earn profits, areas that they didn’t realize before. Such areas include reduction of costs and fact-based decision-making that does not solely rely on gut instinct.
Each business receives overwhelming data from all its business units, which it must use to gain insights on how to improve performance. Consequently, they may desire Data Democratization since they gain the ability to process incredible data amounts and incorporate new technology that assists non-technical users in understanding the data. This post seeks to help you understand the concept of Data Democratization.
Table of Contents
- What is Data Democratization?
- How to Implement Data Democratization?
- How Does Data Democratization Work?
- What are the Challenges During the Implementation of Data Democratization?
- Tech Innovation Propelling Data Democratization
- Concerns about Data Democratization
What is Data Democratization?
Data Democracy is an ideal situation where everyone in an organization has timely and equitable access to data. It allows information in a digital format to be accessible to the average end-user. The goal of Data Democratization is to allow non-specialists to have the ability to gather and analyze data without requiring outside help.
It is the self-service analytics foundation, a tactic that enables non-technical users in any line of business to gather data and conduct analysis without seeking the help of people in the IT department, system administrators, or data stewards.
Data accessibility to everyone means that there are no gatekeepers that guard the gateway to data, making it possible for an organization’s personnel to understand data better, and thus utilize it to reveal exceptional opportunities and accelerate decision-making.
Therefore, the main purpose of Data Democratization is to allow anybody to access and use data at any time when necessary, to majorly participate in decision-making processes without hindrances.
Data Democratization is significant to businesses because it solves three key challenges:
- It enables users to gain access to useful information that is unnecessarily confined in a Data Warehouse.
- It simplifies data searching and unearthing information for non-technical users.
- It provides insights and analysis that the average end-user can understand and use for various corporate actions.
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How to Implement Data Democratization?
The implementation of Data Democratization needs a self-aware structured data program, whose characteristics include:
- Greater access to data by the entire firm.
- Protocols ensure end-users understand the data they are exposed to, such that there are no misunderstandings during interpretation.
- Overall maintenance of data security since more data accessibility increases the risk to its integrity.
Although these safeguards are necessary, they are outweighed by data observation and input from all organizational departments. Further insight takes place effortlessly and drives innovation as well as company performance since participation is encouraged and enabled across a business’s ecosystem.
If any firm wishes to fully embrace Data Democratization, it should internalize the following important concepts:
1. Cultivating Trust
The biggest issue at the beginning of any Data Analytics project is a lack of trust, and the relevant people responsible for building the data stack must face the challenge of instilling it among all end users.
Unconstrained and accelerated access to data empowers all sectors of an organization through the enhancement of decision-making capabilities and thus leads to growth and higher performance.
However, this empowerment could instantly become disillusioned if the system used has extensive data quality issues. Trust issues arise when the exposed data appears to be not credible.
Building a proficient Data Analytics stack, which should last, requires the data pipeline and warehouse to have a solid foundation that may comprise several components.
Such may include proper structuring of the data model’s database tables and implementing a comprehensive dictionary for data. A good example of this is the Amazon Distribution Centre’s indexing system.
A company may opt to use the solution architecture to instill and reinforce trust. It includes technologies and tools that should be included in a typical data stack to show the concerned party’s progress in the mission to solve multiple data source problems.
These tools may include the ETL tool, which Extracts, Transforms, and Loads data into a Data Warehouse that stores data, connectors relating to the ETL procedure, and a visualization layer that lets users probe data even without an understanding of its functionality.
After properly assembling and deploying such components, a firm can achieve the desired results and reinforce trust.
The procedure for Data Democratization includes abandoning the traditional methods and adopting smart Cloud Data Warehousing. It is the best approach because end-users such as non-technical users and analysts access historical data quickly, and integrate, probe, and visualize it with the most suitable tools available.
However, a problem may arise whereby there could be an overwhelming influx of data that is unmanageable if the end user’s comprehension has not been factored into.
Therefore, the structure of the data should be based on business needs and questions. Generally, the data owners whose responsibility is governance should use thought leadership while embracing Data Democratization.
3. Speed to Insight
Historically, IT department personnel have been owning and managing data, causing a delay in business decisions since they are made according to the pace of data access. In adopting Data Democratization data ownership will not change.
However, data accessibility becomes universal throughout the company. If a firm fails to adopt this policy, it is likely to become inefficient, as it is not a pragmatic approach considering that data production will continue exploding exponentially.
How Does Data Democratization Work?
Data Democratization makes it simpler and faster for a company’s staff to access the insights they need, so it is viewed as a game-changer. Equal access to information across departments protects a firm from adopting the Top-Down management approach, whereby the opinions of the highest-paid people are considered more than others.
All people are trusted with more ownership and responsibility of the firm through Data Democratization. As a result, it works through three important factors:
Organizations that are yet to embrace Data Democratization store their data in silos that are spread across Microsoft SQL Servers, files, and partner companies as well as people’s private folders. Consequently, they miss out on optimum company performance because of limited information access.
Cloud-based Data Warehouse creators intend to tear down these silos. The Cloud is a self-contained and fused truth source for Data Analytics, which allows organizations to share aggregated or anonymized data with other parties to increase transparency.
2. Training and Tools
Processing all types of data using a single analytical tool is impossible and so an organization may use multiple tools. Such include tools like Tableau Desktop and Tableau Server and open-source substitutes such as Apache Zeppelin or Caravel of Airbnb.
Others such as the PyData stack that functions on a Docker-based in-house JupyterHub setup are perfect for processing heavy data and various corresponding analyses.
As Data Democratization continues being an empowerment process, a company’s employees must prevent misinterpretation of data. This is achievable through training, whereby trainers create self-study materials for the rest.
The dissemination of expertise may efficiently take place through seminars, mailing lists as well as HipChat channels. Employees can also learn by sitting next to experts in the office.
There is a high probability that large groups of business users need to explore a company’s data more freely and deeply independently. Therefore, multiple training sessions and analytic tools are necessary for such crowds.
A multi-tiered approach is recommendable as it allows different users to acquire the right depths of data, with respect to their needs and skills, which is better than limiting analytics and providing summarized or raw data. The users can utilize dynamic dashboards as the interactive tier to visualize different areas to acquire incremental insights.
The guided analysis experience is also a considerable tier that the analyst prepares for business group users or individuals. The analyst delivers a rich and safe surrounding that enables a few users to utilize explanations and annotations to follow the analysis process.
The visual data discovery tool may also be a necessary step as it facilitates the exploration of broad datasets, and hence replaces the less intuitive methods like SQL queries and data tables. Microsoft Excel may also be a great alternative for users to convey data easily. An internal certification course may help avert users from misinterpretation and misuse of data during greater data access levels.
People that are open, inquisitive, positive, and persistent have the type of mentality required in Data Analytics expertise. Companies acknowledge these types of people and reward them during hiring or appraisal processes.
They motivate and engage these persons so that they can continually be creative with their thoughts, and thus manipulate data while asking all the necessary questions. Experts receive invites to coordinate seminars as they educate others on tools, key concepts, and new advancements in technology.
What are the Challenges During the Implementation of Data Democratization?
Implementation of Data Democratization is not without challenges, and the main challenge is that data teams struggle with keeping up with the data demand from all company areas. As this demand increases throughout an organization, understanding the said data requires more complicated analysis.
Many firms face this issue and hence want to provide all employees with chances to be data-driven, but again lack adequate resources for the task.
Luckily, self-service analytics emergence is a perfect solution for this problem because it makes data available to users at any time, thus making each employee a data scientist. Self-service analytics takes place through dashboards that provide real-time data, but are only wholly implementable through analysis tools.
As a company incorporates this technology, it must provide training for its staff so that it can bring real value, and thus make Data Democratization highly efficient.
Tech Innovation Propelling Data Democratization
Data Democratization continues to be attractive due to the immense amount of data created, which is often referred to as Big Data. As technological advancements take place, there have been multiple emerging tech innovations that are helping non-technical people make sense of information. The following are examples of such tech innovations:
- Cloud Storage
- Data Federation Software
- Data Virtualization Software
- Self-Service BI Applications
- Cloud-Based ETL/ELT SaaS Platforms
1. Cloud Storage
Organizations are using Cloud storage as the central location for holding data, and thus are avoiding data silos that prevented the implementation of Data Democratization in the past. To heighten security, the Database Management security features masking or encryption of the data.
2. Data Federation Software
This software combines data from numerous sources into a virtual database using metadata.
3. Data Virtualization Software
This software retrieves data and then manipulates it even without knowing about its technicality. This assists in avoiding unnecessary actions such as cleaning up data inconsistencies or different file formats.
4. Self-Service BI Applications
These are applications that simplify the interpretation of Data Analysis for non-technical users. They appear as machines that host data and then simplify it as visualizations for non-technical users to understand.
5. Cloud-Based ETL/ELT SaaS Platforms
These platforms are enabling businesses to stream and batch-load data from hundreds of data sources and map this data to Data Warehouses for last-mile analysis.
Concerns about Data Democratization
Although Data Democratization can help organizations to become more productive, many still hold reservations about non-technical employees misinterpreting the data, which would lead to bad decision-making. They are worried about data security risks and maintenance of data integrity due to more users having access to the company’s information.
As a result, some firms are reluctant to move sensitive data from silos, and although the majority have made improvements in recent years, this issue continually makes it difficult for staff in various departments to view data. Other concerns include effort duplication across diverse teams, which would eventually cost organizations more resources compared to centralized analysis groups.
The usage of Data Democratization ushers in a new era where businesses can function better. Any business that embraces it requires rigid governance to guarantee the best management of data. It should properly train its employees on how to use the data to make important steps that can drive the firm’s progress and initiatives. Every individual in this evolution will have a small win when users without technical knowledge access data and gain insight since it will ultimately prove beneficial.
Since the concept is still in its early stages, more time is needed to discover Data Democratization’s full impact across all enterprises. However, positive-minded people have hope that it will change how decision-making takes place in companies as employees gain access to all levels of data collected, and acquire insights for future actions.
Data-driven organizations that are embracing Data Democratization must acknowledge that it is a slow procedure, whereby incremental transformations in culture lead to small wins that boost other cultural changes. Presently, more organizations are trying to use this concept to give their employees access to data, which is helping in the enhancement of job performance and overall organizational health.
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