Data intelligence is the process by which various methods and tools are used to interpret and analyze data; translating large masses of data into valuable, actionable insights for businesses and organizations. Leveraging the power of intelligence, the process of data intelligence not only provides an explanation for what data is, but also overarching insight into what data means for businesses and organizations.
But in today’s complicated digital landscape, this description of data intelligence gives rise to important questions. What benefits or advantages can data intelligence provide to a business or organization? Moving forward into the future, what opportunities could be missed if data intelligence processes are not leveraged? And how does a business or organization that is not currently harnessing data intelligence successfully implement such processes in an impactful way?
This article aims to answer all these data intelligence questions and more, providing insight into the multitude of strategic functions and use cases that data intelligence can fulfill within an organization. Today, the implementation of data intelligence is not only beneficial, but realistic and doable for organizations of any size.
Data intelligence originates from use cases relating to analytical processes within businesses and organizations. In the past, it was a struggle for analysts to locate and identify the data they needed to create reports about, and extract insights from, data. Early data intelligence harnessed metadata to make information more easily discoverable. This saved data analysts a significant amount of time, increased operational efficiency, and paved the way for data intelligence to grow and evolve.
Eventually, harnessing data intelligence to streamline data discovery processes alone was no longer sufficient. The importance of data collection increased—and data collection itself scaled exponentially—and analysts suddenly had to draw valuable insights from unprecedentedly massive amounts of information. This proved to be a challenge for human analysts and, in response, data intelligence processes and methods evolved to fill these emerging needs in the marketplace.
Today, data intelligence allows businesses and organizations to harness the power of artificial intelligence and machine learning for a multitude of practical use cases, analyze data at a scale and speed beyond human capability, and make deeply informed, evidence-based operational decisions.
By providing structure to unstructured masses of data—making datasets sortable and easily understood by software—data intelligence can serve many functions within an organization. It can be used to reduce costs related to data storage, save time with data discovery, diagnose looming issues, improve security, and mitigate risks and provide an evidentiary basis for important business decisions. Put simply: when properly implemented, data intelligence can maximize the value and utility of data within an organization.
Over time, as data intelligence has grown and developed, five different types of data intelligence methods and processes have emerged: identifiable, predictive, diagnostic, optimized, and prescriptive. These distinct categories of data intelligence work together, employing artificial intelligence and machine learning to maximize the value of a business’ data.
Identifiable data intelligence harnesses metadata to locate and assess unstructured data. As it is focused heavily on providing structure to unstructured data, identifiable data intelligence is typically the first type of data intelligence implemented by a business or organization.
Often, data within businesses and organizations is unstructured; it is stored in multiple locations, without overarching organizational systems in place to make sense of it all. A lack of structure in datasets negatively impacts the efficiency of all data-related functions within an organization.
Identifiable data intelligence addresses these issues directly. By referencing metadata, identifiable data intelligence can analyze data relationships at scale and identify larger patterns and trends. These insights can help to provide structure and organization to otherwise unstructured datasets.
Identifiable data intelligence can provide a multitude of benefits within an organization. It can uncover data that’s unknown, unused, or untapped. It can specify redundant, obsolete, or trivial (ROT) data that can be deleted to reduce storage footprints and costs. It can pinpoint and act on data to guide next steps, and, ultimately, provide amore streamlined and overarching understanding of the meaning of data within a business.
Predictive data intelligence applies statistical learnings and algorithms to massive sets of data to predict future possibilities and outcomes. This allows for businesses and organizations to understand historical trends and, through comparison, gain insight into current business operations.
By viewing historical data through the lens of artificial intelligence, predictive data intelligence provides new and valuable insights into the past, translating data into valuable insights about the historical successes and failures of a business or organization.
Diagnostic data intelligence interprets data with the end goal of providing insights to the root causes of issues within a business or organization. It identifies anomalies in data, through interpretation and analysis, to provide valuable information about why such anomalies occur. Harnessing intelligence and machine learning, diagnostic data intelligence allows for the efficient remediation of internal problems which, in turn, advances future successes.
When issues within a business or organization are left unidentified, they can fester, ripple out into adjacent areas, and jeopardize operations. By using intelligence to root out the causes of issues, diagnostic data intelligence can help businesses and organizations gain insight about internal problems, providing an evidence-based foundation for proactive, preventative decisions to avoid them altogether in the future.
Optimized data intelligence interprets data to drive greater productivity and cost savings within a business or organization. It strategically aims to drive down costs by identifying, and deleting, unnecessary data to reduce data storage requirements and associated costs. This can help businesses and organizations reallocate saved monies to other areas of need.
In addition, deleting unnecessary data increases the overall quality of datasets. By deleting ROT data and providing data structure, optimized data intelligence increases the overall value of data within a business or organization by allowing for more valuable insights to be drawn in all analytical processes.
In addition to cost savings, optimized data intelligence uses intelligent classification to improve and streamline internal business operations. It can be used to develop policy-based, automated data actions to increase efficiency, reduce employee workloads, and lower the risk of human error. Optimized data intelligence can also streamline data management; automating data governance rules and ensuring that individual teams can access the specific data that they need. This not only promotes efficiency, but also ensures data integrity and security within an organization.
Prescriptive data intelligence uses machine learning to provide new insights from data, and unique recommendations for data-based decisions within a business or organization. Whereas the other areas of data intelligence are oriented towards providing insight and information to enable and inform human decision making, prescriptive data intelligence goes one step further. It analyzes and interprets trends within datasets to develop actionable recommendations regarding strategic decision making.
As such, prescriptive data intelligence can have a significant impact on several areas within a business or organization, from revenue and operations to security and compliance. It leverages intelligence to provide insight into not only the significance and meaning of data trends to a business, but also what (if any) actions should be taken in response to such trends.
Often, datasets within organizations are unstructured. In addition, anywhere from 25 to 80% of a given organization’s data is likely redundant, outdated, or trivial. Cloud migration is a data intelligence process that addresses both issues by first deleting unnecessary data, and then remaining, necessary data to a structured, organized cloud storage system.
Cloud migration serves many purposes within a business or organization. It saves money by reducing data storage requirements, improves security by categorizing and protecting high-value and classified information, mitigates compliance risk by identifying high-risk data types like personal information and, lastly, ensures proper access and retention for sensitive files before, during, and post migration.
Businesses need to make sense of large datasets. Cloud migration can significantly help by identifying and deleting useless data, moving important data to a single, secure location, and providing the structure organization necessary to successfully implement data intelligence within a business or organization.
It is crucial that businesses store data in accordance with regional, legislative, and industry compliance requirements. To do otherwise leaves an organization vulnerable to liability, and puts sensitive information at risk. Data intelligence can help with governance and compliance issues in a multitude of ways. It can run regular audits to analyze compliance and correct for anomalies, apply industry-specific compliance policies where appropriate, automate the compliant deletion, copying, and movement of files and, most importantly, stay current on regulatory and legislative changes, automatically implementing new compliance requirements into data structures as they arise.
A violation of compliance requirements can lead a business or organization to significant losses. But, data intelligence can be implemented to help businesses mitigate this potential risk. By directing intelligence to promote, and maintain, data compliance standards and practices, a business or organization can protect itself from liability while also promoting the privacy and security of customer information.
Storing massive amount electronic information requires a massive amount of energy. Data intelligence can be used to encourage and promote sustainable data practices within a business or organization in ways that not only reduce the environmental impact of business operations, but also save money on data storage and management.
The deletion of ROT data can reduce a business or organization’s storage footprint by an average of 40%, having a direct impact on energy usage and sustainability within a business. In addition, cloud storage suppliers are often streamlined to maximize data storage while simultaneously minimizing overall energy consumption.
Cloud data migrations can reduce energy consumption by 65% and carbon emissions by 84%. This illustrates the positive impact of data science on the sustainability and environmental impact of a business; it not only reduces bottom-line cost, but reduces the negative environmental results that stem from unstructured data strategies.
Mergers and acquisitions tend to be drawn-out, complicated processes. The integration of two separate firms (or the absorption of one firm into another) comes with a wide range of logistical, analytical, and organizational challenges. But, data-related challenges in such situations can be managed, streamlined, and simplified using data intelligence.
By storing data on a structured cloud with proper categorization, data intelligence processes can help facilitate the identification and transfer of necessary information in a merger or acquisition scenario. Intelligent classification tools can automatically identify files that are—and are not—relevant to a merger or acquisition, making sure that a company only shares information necessitated by the agreement. In addition, the positive impacts of data intelligence in mergers and acquisitions extend far beyond data itself. By automating a bulk of the sharing and transfer of data in such a situation, organizations can delegate more human attention and effort to other challenges or opportunities in a merger or acquisition scenario.
Data collection has exploded in the past twenty years to unprecedented levels. Over time, increases in data collection gave rise to emerging needs in the marketplace. Analysts still had to do their jobs, but massive increases in data volume necessitated the development of tools to increase efficiency and productivity. When the amounts of data being collected by businesses began to exceed human analysts’ ability to make sense of it all, software companies responded.
The emergence of the above-described trend resulted in the creation of data intelligence software: computer programs that leverage artificial intelligence and machine learning to automate the management and analysis of massive datasets. Today, data intelligence software has become increasingly indispensable to businesses, increasing the value and importance of data within business operations.
Aparavi, a cloud-based data intelligence software platform, is one such software platform. It unlocks the value of data by providing structure to unstructured datasets and, leveraging intelligent classification within this structure to maximize the usefulness of data for businesses and organizations. Data intelligence software such as Aparavi connects businesses and IT in a way that transforms raw data into valuable assets that can support organizations in pursuit of their main business objectives.
The term “unstructured data” refers to datasets with a lack of overarching organizational systems. This lack of organization makes unstructured data difficult to analyze and understand; much like how it is difficult to find an object in a messy, disorganized room. The difficulties in drawing insights from unstructured data greatly limits unstructured data’s value within a business; how can valuable insights be drawn from data which lacks the organization and structure needed to make it digestible?
The key to success for any business or organization to manage and maximize data usage is structure—a system of data structure that serves the organization’s data-related needs and overall business objectives. When data is stored, sorted, named, and tagged with business operations in mind, AI machine learning can be implemented within this kind of structure to great benefit.
It is for this reason that Aparavi’s approach to data intelligence is centered on structuring data first, later intelligent classification to work within this structure to translate datasets into valuable, meaningful information for businesses and organizations.
AI and data intelligence are revolutionizing how businesses and organizations harness internal data. In today’s world, efficiency and speed are valued at a premium, and the strategic combination of AI and data intelligence can both speed up and scale the usefulness and application of data within a business. The problem comes into place when data intelligence software relies more on AI than on custom policies created and provided to a dataset. Aparavi harnesses the power of intelligent classification policies to structure datasets in the most precise format possible.
Data is the most valuable commodity in the business world today. But data’s value is limited by a business’s ability to interpret and gain insight from trends and patterns. AI algorithms alone are not the most powerful tools available to gain such insight. Businesses or organizations that are solely relying on AI with no BI solutions leveraged, are likely operating far below maximum efficiency and missing out on key opportunities. Instead, intelligent data management software make data valuable by analyzing, interpreting, and translating data at a scale and speed that human analysts can’t possibly match – with little to zero room for error, and no learning time required.
BI (business intelligence) analyzes and interprets data to help businesses and organizations serve their business objectives through evidence-based decisions. Harnessing both predictive and prescriptive data intelligence, BI solutions can help to empower teams with access to relevant data that enables them to make data-based decisions in consideration of business operations and goals. By leveraging predictive and prescriptive data intelligence in light of business operations and objectives, BI helps businesses better achieve their goals and objectives with data-driven, real-time decisions.
In the modern business world, data intelligence can provide numerous advantages to organizations of any size. Data collection is now a common and widespread business practice. Data intelligence maximizes the value of data within any business or organization by leveraging intelligent classification to translate raw data into insightful, useful information.
In emphasizing the importance of providing structure to data, data intelligence helps businesses and organizations save time, money, and labor resources. The elimination of ROT data reduces storage footprint, saving money on storage. Cloud migration provides a structure to datasets that saves time during data discovery and analytics processes and, lastly, automation of data management allows for human workers to focus on higher value tasks within the organization.
Data intelligence can also benefit organizations by improving data security, ensuring compliance, and protecting them from potential liabilities. A data breach or non-compliance lawsuit can present major problems. But sound data intelligence practices can greatly reduce these risks. By maintaining data structure, prioritizing the protection of sensitive data, and staying current on compliance requirements, data intelligence can benefit an organization not just by mitigating risk, but by proactively automating the maintenance of data security and compliance.
The most significant benefit of data intelligence to businesses, however, is that it provides a valuable, evidentiary basis for making decisions within an organization. Interpreting and dissecting raw data at a modern scale would be impossible for any individual person to manage themselves. By leveraging intelligent classification, data intelligence transforms raw datasets into valuable information that can be used to the benefit of any organization’s overall goals and objectives.
The undeniable benefits that data intelligence can provide to a business give rise to a single question: how does an organization go about implementing data intelligence practices? By partnering with the right data intelligence platform for its goals, organizations can implement data intelligence in three steps.
First, to implement data intelligence within an organization, leaders within the organization must develop a data intelligence plan with achievable goals and identify use cases that could practically serve business objectives and needs. This is an important step because use cases can vary business to business based on vertical and size. For instance, a hospital group would likely have more use cases related to compliance and security than a chain of ice cream shops when implementing data intelligence to the organization. In the implementation phase, it is important to keep in mind the specific nature of an organization when developing goals and potential use cases for data intelligence.
Once use cases are identified and goals are developed, a business needs to test these use cases with its chosen data intelligence software platform. In this phase, a small test group needs to run experiments to determine the feasibility of identified use cases, and the attainability of implementation goals that were set in the first step.
If early implementation tests go well, the next step is to implement usage of the data intelligence platform—first with a small group of teams or individuals, scaling and branching out as more people become comfortable and competent with the new software and its use cases.
Once the decision for implementation has been made, scaling implementation across an entire organization and onboarding teams and employees may seem like a complicated task. While there are certainly moving parts to it, organizations can successfully implement data intelligence by focusing on a few key areas.
Early on, be sure to select enthusiastic team members to be early adopters of the software. Ultimately, these individuals will serve as leaders in the implementation process, and getting them onboarded early in the process can aid further scaling as data intelligence implementation progresses.
As data intelligence implementation is scaled, identify what does and doesn’t work. Diagnose and fix operational problems and inefficiencies as they arise. Focus on how systems are working and intervene to pivot as needed to best serve the organization’s objectives.
Once data intelligence software is being used by a majority (or all) of the company, begin to assign roles to teams and individuals, and review data as needed to continually identify, optimize, and improve use cases for the data intelligence platform within the business.
Once an organization decides on the implementation of data intelligence, and is ready to begin implementation, only one question remains: what features should a data intelligence platform provide? Ultimately, there are four significant features that a business should look for when deciding upon a data intelligence platform: data identification, data migration, data classification, and data optimization.
The ability to identify and provide insight into data—specifically into which data is ROT and which is valuable to an organization—is a critical component to data intelligence. Maintaining the quality of datasets ensures the quality of insights that can be extracted from data. To maintain this level of data and insight quality, it is important that an organization’s chosen data intelligence platform can identify ROT data.
Structure can be provided to unstructured data through migration processes; data can be taken from multiple locations and migrated to a single, centralized, manageable location. Migration is a critical component to implementing data intelligence, because it improves structure and, ultimately, allows intelligence to make better inferences from datasets.
Classification—categorizing and sorting data—provides risk mitigation and protection benefits that are essential to any business. Classification allows businesses to use data intelligence to protect sensitive data, mitigate risks of liability or data breach, and maintain compliance.
A data intelligence platform needs to fit within an organization’s operational needs and business objectives. For this reason, optimization is the final, critical component to deciding. Data optimization leverages automation to manage datasets in accordance with the needs and objectives of an organization, allowing for custom classifications, rules, and insights tailored to a specific organization.
Aparavi is a data intelligence platform that helps businesses to harness the power of data through all four of these methods. From structuring unstructured data, to increasing efficiency, to mitigating risks and serving greater organizational goals, Aparavi provides benefits to organizations looking to harness the power of data intelligence.