The concept of ‘sensitive data discovery’ implies the method of collecting data from various external data sources by recognizing certain patterns or outliers. In the broader sense, this process helps in consolidation and subsequent categorisation of the data into similar subjects.
In the modern competitive business environment, every organization needs to derive meaningful information. The raw data gathered from a variety of sources using business intelligence platforms. But ‘data discovery’ is not itself a tool, software, or platform.
It is the basic concept upon which marketing organizations and business owners in this contemporary world survive and thrive. However, there are several data discovery tools available in the market. The ability to derive valuable information and changing business patterns from raw data gives a company the required competitive edge in the market and boost to business performance.
Besides, in comparison to traditional business intelligence approaches, these tools add value to organizations. Also, enhance the market reputation of these companies.
The levels of data discovery can be categorized into the following steps –
- Preparing data received from external sources for analysis.
- Visually analyzing the data and
- Advanced analytics is performed by various tools.
In simple words, data discovery tools are software that business analysts use in the business processes for data analysis. This enables the company to stay well-informed while taking important business decisions.
Tools that fall under the category of data discovery tools are the following:-
- Business Intelligence tools.
- Tools for statistical analysis.
- Programming languages for general working.
- SQL Consoles
- Predictive Analysis tools.
Data Discovery Tools for Smart Data: Influence on business Revenue
The term ‘smart data discovery‘ was coined by Gartner. It has a major influence on the software programming industry. According to this company, a smart data discovery or discovery process implies a revised ability of data discovery. It also enables any non-technical users to access the insights derived from any dataset.
The advantages of Smart / Sensitive data discovery are as follows:-
- Understanding – Data discovery, to begin with, is beneficial as it provides a clear understanding of the data, the source from which it was received, and the potential use of it.
- Classification – When data discovery leads to the derivation of smart data, it is automatically classified based on the context.
- Risk Management – This process helps in better management of risks and initiates improved compliance with the regulations.
- Transparency – In this process, the complete data is transparent and visible.
- Sensitive data handling – The method enables the better identification, classification, and handling of sensitive data.
- Using protective measures – Organizations can use protective measures to protect sensitive data from leaking.
Kinds of Data Discovery Tools :
In the contemporary business market, data discovery software or solutions can be categorised into the following:-
- Textual searches tools
- Tools for visual interaction and
- Tools based on Analytic Intelligence or AI
The tools for textual searches or search-based data discovery tools are known as ‘search engine like’ tools. Moreover, they form a special segment among the tools created for data discovery. These perform the specific function of searching data or information based on certain words.
The tools perform the function of search-based data discovery based on some keywords. This segment of tools is particularly used by companies or organizations that work with huge amounts of unstructured data. It may seem that big organizations working on structured data have no use of these tools.
But this is highly incorrect. In the absence of these tools employees of any big organization may be unable to find data files. Piles of structured and unstructured data remain unuseful.
Additionally, the visual interaction tools access the data directly without going through any other protective layer of information. It creates visual analytics easy to understand. Visualization-based data discovery tools enable users to analyze large sets of data. You can recognize patterns or trends in the information, visible on the screen and create interactive business reports backed by historical data.
The only aim of visual data discovery is not only to analyze numerically and formulate charts based on them. Rather, you can use these tools to create graphic interfaces. The users analyze and formulate reports and dashboards based on interactive visualization.
The tools based on Analytic Intelligence or AI deploy machine learning patterns for spotting the trends, predictive analytics, or patterns in the market and business models. These analytics tools then output the patterns in the form of creative and easily understood visuals for taking actionable insights.
Microsoft BI Capabilities as a Data Discovery Tool :
The primary function of any data discovery tool is to analyze the data. It provides valuable insights into the changing patterns and trends in business. In this context, the discovery capabilities of Power BI are more advantageous in comparison to other tools. The platform enables a user to run quick insights that aid in subsequent visual data discovery using a visual interface.
In Microsoft’s BI, the user organization can run the process of finding insights on a selected dashboard or report as well as interact with the insights already created. This graphical interface created by the system helps organizations to stay well-informed about the market while taking crucial business decisions. Microsoft has built the feature of insights based on several advanced algorithms with the aid of its researchers.
The organization continues to provide businesses all over the world, the necessary assistance required to derive value from their data in more interactive ways.
Meaning of the term ‘Semantic Layer’ :
The concept of ‘Semantic Layer‘ refers to a presentation of corporate data in a proprietary data structure. The use of this kind of representation of business data lies in the advantage end users derive from it. In simple words, when a certain organization structures its data in the semantic layer, the end-users can access this data further anonymously.
The major benefit of this set-up is that it provides access to data with the use of simple business terms. You can implement semantic layers in organizations to simplify the process of consolidating complex data into simple terms like a customer, product, revenue, and others. The use of this structure thus helps the unified understanding of data all through the organization. You can read more about semantic layers models in Business Intelligence.
Using the semantic layer and substituting complex computer language with common business terms, an organization can simplify complex data. Accessing data in the semantic layer provides a unified common feeling to the users by providing data stored in relational databases and OLAP cubes. This is considered to be the core of the concept of business intelligence.
In the Microsoft BI, the Semantic Layer is considered as a storehouse of intelligence. It serves the purpose of deriving value from a dataset and transforms it into an easily understandable standard. Subsequently, You can load this data in Power BI, Excel, or any other mechanism with business intelligence. This layer is structured to ready the datasets in a simpler way which can be analysed by Power BI later.
Microsoft deploys the Semantic Layer in an organization with the following deliverables:-
- A document of design that contains the best practices for configuring the Azure Analysis Services.
- Configuring and deploying the Azure Analysis Services.
- Creating a minimum viable data model in the Azure service.
Microsoft BI as a user-adaptive software :
Power BI being a cloud applications (SAAS BI) business intelligence service, provides insights on various datasets. A person with basic computer knowledge can use this system in order to derive value. No matter the raw data that is available from external or internal sources.
The system is a collection of several business intelligence tools, for instance, software services, connectors for data, and other applications. In addition, you can deploy the Microsoft BI system on-premise or use it as a cloud service available through Power BI Service
While a service or system is bound to have both advantages and disadvantages, Power BI has a major advantage over other BI applications on one ground. In comparison to other BI tools, Microsoft’s BI is comparatively more user-adaptive with more data governance control. It is built for interactive visualizations and smart visualizations. The graphical user interface is built with drag and drop facilities compiled with the self-service capabilities of the system. It also enables the user to perform data analysis functions swiftly and with ease.
The Excel integration facility in this system furthers this advantage by creating an intuitive interface. Power BI can now analyze the data and help these users to integrate and present this data subsequently in Excel sheets. It becomes big convenience for their organization for user adoption.
Moreover, with functions like natural language processing, the affordability of the BI system of Microsoft is hardly matched by any other tool in the market.
Further, you can access these created reports, and dashboards, edit, share and interact with amongst the employees in an organization.
Process of Data Discovery and Insights:
The process of data discovery insights have the following pattern :-
- More options from the datasets have to be selected. Then the Get Quick Insights has to be chosen.
- Various advanced algorithms are available.
- Within a few seconds, the insights from the various datasets are ready to be accessed.
Besides, the insight cards created by the above process are also capable of interaction. The user can easily share and interact within these insight cards with fellow employees in an organization.
What are the Challenges of Data Discovery :
The increasing implementation of data discovery tools by organizations in the modern market has its own set of disadvantages. These can be categorized as the following –
Expert or Casual pattern spotting:
The increasing dependency of organizations upon tools and software is alarming. This refers to the fact that companies in recent years have grown dependent on tools for spotting the changing patterns and trends in the market.
Consequently, the results depict that sometimes even these sophisticated tools are unable to spot the most important of the trends. Hence, We at EpCGroup Power BI consultation strongly suggest that along with the use of developed data discovery tools, companies must employ analysts with deep trend discovery capabilities.
Genuine or biased stories:
An increasingly popular pattern of business intelligence is data storytelling. This refers to presenting the relevant data in the form of stories for better retention of information. If a certain product or service campaigns in the form of a story, it must be catchy to make it acceptable to the audience. To make the product campaign interesting, a company might have to present the facts of the same in a captivating manner.
But committing some facts or misinterpreting them in any way is not permissible. This is because customers require complete information to make well-thought-out decisions. Thus, fake or biased data story campaigns may negatively impact a business’ reputation.
Mixed or unfiltered data:
To combine various statistical datasets, organizations require to strictly complete the process of documentation. Additionally, the presentation of pure data regarding a product or service enhances the reputation of an organization. The process of publishing unfiltered pure data is as follows –
- Providing information on sources
- Presenting documentation on all transformations of the data.
- Listings the levels of integrating the data
- Deriving information from original data
- Integrating the datasets
- If the data presented in front of the customers is not pure, the reputation of the organization can be potentially damaged.
What the different trending Data Discovery Tools :
The following are the currently trending data discovery tools in the market. Along with this a comparative analysis of Power BI is provided.
Domo
Similar to Power BI, the Domo business tool also performs similar functions. But in comparison to Microsoft BI, the system is less popular among customers with a rating of 4.23/5 and about 184 reviews.
Dundas BI
Although Dundas BI is currently one of the trending BI apps, it is less cost-effective than Power BI. The former has an on-quote subscription pack while the latter’s subscription starts from $9.99.
Sisense
One of the most trending apps of BI, Sisense has several attractive features for user organizations. But it is lacking in comparison to Power BI on the grounds of interactive reports. Sisense does not have this feature.
Alteryx Connect
Alteryx Connect comes with various strong data abilities. But in comparison to Power BI, it is more expensive as Microsoft BI has cheaper subscriptions and even some free tool options.
Informatica
Informatica has high-performance applications similar to Power BI. But, the latter is more popular in the market because it can be operated throughout all mobile devices. Moreover, this enables the user to access the organizational data from anywhere.
Sigma
Similar to Power BI in its applications, Sigma lacks behind Microsoft’s BI in terms of loading and providing interactive reports in real-time.
Conclusion :
Thus, it can be concluded that Power BI is the go-to data discovery tool for your organization. It has affordable subscriptions and advanced abilities of analysis.
