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Data democratization strategy for machine learning enterprise
In the enterprise, data democratization works to break down data silos by opening access to an organization's data across teams in an effort to improve workflows.
The amount of data that organizations collect has grown tremendously over the last decade. With this comes increasing challenges for organizations in the way that they store, access and govern data effectively so the correct groups of people have access to the data they need.
Yet, many of the traditional ways of dealing with data create bottlenecks, silos and data trust issues. Through providing more widespread access and control of data, companies can ensure successful data strategies by removing the bottleneck to data access, improving employee knowledge, removing data silos. This approach of "democratizing" data access is what is needed to handle the almost insurmountable amount of data.
Data democratization and what works against it
Data democratization is the ability for data to be accessible, usable and manageable in a digital format to all users and stakeholders within an organization. This allows non-specialists the ability to access and analyze data without requiring help from the IT department.
As organizations are adopting machine learning and AI into their workflows and using these results to help with decision-making, the need to access a wide range of data sets and relevant data becomes increasingly important. By making all data readily available, they are able to build accurate machine learning models that can continually learn with relevant, up-to-date data over time. While this concept sounds simple, data democratization in practice is not easy.
The main difficulty in achieving a successful data democratization strategy is the tendency for organizations to keep their data in silos specific to and only accessible by individual departments. These silos are present because of legacy data collection practices and traditional security issues, but they can make access difficult across teams.
This collection instructs data scientists on how to create data sets for machine learning, including features on limited data, ethical implications of synthetic data and how to democratize data.
How to create a data set for machine learning with limited data
Labeled data brings machine learning applications to life
Synthetic data for machine learning combats privacy, bias issues
Internal data governance policies may also create access issues as certain data sets may be limited to only a few internal groups or senior leadership. This could be because the data contains sensitive user or customer information, sensitive business information or data that the company may otherwise not want to give widespread access to.
Making data readily available
In order to achieve data democratization changes to data collection practices, data governance frameworks and even corporate culture may be needed. Fortunately, there are tools and technologies that enterprises can employ to help organizations break down bottlenecks to data access and allow them to improve their internal data access and work towards data democratization.
Cloud storage has provided some solutions to data democratization challenges by allowing data to be stored in one central location, helping eliminate data silos between departments or teams. Cloud storage can help give multiple departments the same access to data and through it better understand their customers, market and competitors.
Data virtualization and visualization tools are also now allowing users with varying levels of data expertise to retrieve and manipulate data. Virtualization provides users access to structured and unstructured data from a variety of different sources. It automatically aggregates the data without requiring users to know or understand technical details, such as how the data is formatted or where it is physically located or stored.
Data visualization tools also allow the user to transform data into a visual context, such as a map or graph, helping to gain insights. Graphs and charts can convey trends, anomalies and other insights in a clearer fashion than rows and rows of numbers or text.
Augmented analytics
Vendors have also simplified their tools by producing easier-to-use analytics tools that can help with a data democratization strategy by allowing employees to generate deeper insights from the data. In democratizing data access, companies have found that automated systems provide the ability to build easy-to-use interfaces for businesses users who may not have the technical skills needed for traditional analytics tools. This augmented approach makes it easier for the non-technical user to derive insights from the data, break down traditional data silos and help work around data access issues that might otherwise occur.
Augmented analytics also allows teams to make decisions faster. These analytic tools help employees access organization-wide data with minimal help from IT. No longer do teams need to request and then wait for access. They are able to quickly access data to discover trends, insights, and collaborate with colleagues anywhere and anytime they choose.
Augmented analytics also allows for all teams at an organization to become more data-driven by leveraging AI and machine learning. According to analyst firm Gartner, augmented analytics is "... a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists."
Since technical expertise is no longer required to access data, line-of-business teams who may have data science or engineering skills are now able to leverage AI techniques to transform the content of their data in ways not possible before.
Ensuring employee data competency
In the typical organization, the majority of employees are still not sufficiently knowledgeable on methods to effectively use traditional data analytics tools. Therefore, for the non-technical and line-of-business employees, data democratization is allowing data to be accessed safely to the entire company and no longer needed to be protected behind company "gatekeepers."
Employee data competency is a critical component of a company's plans as they move forward. By allowing data access to employees at all levels, you empower your employees to become more data-driven. This reduces the impulse to make decisions based on instinct or use obsolete approaches. Stakeholders are able to access data on an as-needed basis with easy-to-use tools for analysis and make informed decisions from the results provided.
Businesses that want to adopt data democratization need to be more intentional in their efforts. That means transforming company culture, budgeting for necessary technological and software tools and training employees. Organizations need to provide sufficient support to successfully use these tools and become competent with data to help drive the organization forward.
