Business Intelligence ​and Data Analytics

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Technoforte is a Data Science company with more than 23+ years of Business Intelligence and Advanced Analytics implementation experience for customers across multiple domains.

What is Business Intelligence and Data Analytics?

Business Intelligence and Data Analytics are terms used to describe the procedures, methods, and equipment that are used by organisations to gather, examine, and decipher data in order to obtain knowledge, create wise judgements, and accomplish corporate goals. Organisations can leverage useful information produced by business analytics & business intelligence software to improve strategic planning, streamline operations, and spot growth possibilities.

Data from many sources, including internal transactional systems, external data sources, and third-party data providers, is frequently used in business intelligence and data analytics. Through the use of data modelling, data integration, and data cleaning processes, this data is transformed into information. After the data has been prepared, it can be analysed and visualised using a variety of analytical approaches, including advanced analytics, reporting, dashboards, and data visualisation.

Business Intelligence and Data Analytics

The process of analysing data to find patterns, trends, and insights that might guide decision-making is known as business analytics. Business Intelligence and Data Analytics can be diagnostic, predictive, prescriptive, or descriptive. Diagnostic analytics involves analysing historical data to understand what happened in the past. Predictive analytics uses data to make forecasts or predictions about the future. Prescriptive analytics involves recommending actions to maximise outcomes.

Finance, marketing, sales, supply chain management, human resources, and operations are just a few of the company functions that use business intelligence tools and analytics. Businesses utilise business analytics & business intelligence software to learn more about their operations, clients, markets, and rivals. These insights help them plan strategically, streamline their operations, and boost performance.

With the introduction of new technologies like big data, artificial intelligence (AI), machine learning, and sophisticated data visualisation tools in recent years, the discipline of business intelligence and data analytics has advanced. Organisations are now able to handle and analyse massive amounts of data in real-time, leading to the discovery of fresh insights and potential for innovation.

What we do:

Business Intelligence and Data Analytics

Data Extraction, Transformation, Loading and Storage

Technoforte provides data cleansing services. Easy availability of clean data from multiple sources into one comprehensive database is essential for any business analytics need.  Extract, transform, load (ETL) and extract, load, transform (E-LT) are the two main approaches used to build a data warehouse.

Business Intelligence and Data Analytics

Data Analytics & Design

Turn the data from multiple sources into actionable insights for decision-making. Technoforte understands your reporting requirements and how the team uses the dashboards at various levels of the organization. The summary graphs can be drilled down to the required information by the users. The right visualization (graphs, bars, charts, lines, gauge, funnel, maps etc.) is used based on the user requirement. Self-service BI is suggested and implemented to give more power to the tech savvy business users. A question and answer visual can also be designed using Natural Language.    

Business Intelligence and Data Analytics

Predictive Analytics

Making predictions based on historical data and analytics techniques such as statistical modelling and machine learning. A binary prediction model can be created using dataflow and training the entities in the dataflow for machine learning. This can help create scored data reports for better prediction. Technoforte BI team can help customers setup dataflows, link tables from other dataflows and set up s predictive model using tools such as Microsoft Power BI, Qlik Sense or Tableau.

Business Intelligence and Data Analytics

Big Data Analytics

Analyse high (growing) volumes of structured transaction data and unstructured data that are often left untapped by conventional BI and analytics program. With ease of data collection and storage, the challenge is how effective this data is used by the organization. The key is to be able to store and organize data and also use it for data insights at the same time. Cutting edge data warehouse tools like Snowflake and BigQuery are used for this purpose. The Technoforte team can help companies collect data from disparate data sources, store and cleanse data, understand company’s requirement  and form data sets, identify trends, patterns and data relations, setup a Machine Learning Model and implement the relevant visualization tools.

Business Intelligence and Data Analytics

Data Governance

Technoforte team can consult organizations with data governance policies and selection/implementation of right tools, which can help users across the organization in effective use of accumulated business data. This will be keeping in mind data security, quality data availability and compliance with regulatory bodies. For example, setting up a data warehouse helps right from data collection, data storage, data availability, data retirement, data security and policy compliance. Good data governance not only drives data democratization, but also increases employees and customers trust, helps data driven decision making and increases an organizations brand value.

Data Integration and Data Migration

Data integration and migration are critical aspects of business intelligence and data analytics, as they involve the process of extracting, transforming, and loading (ETL) data from various sources into a data warehouse or analytics platform for analysis and reporting. Here are some key considerations for data integration and migration specific to business intelligence and data analytics:

  • Connectivity to Data Sources: Business intelligence and data analytics frequently require connectivity to a variety of data sources, including databases, data lakes, cloud storage, APIs, and more. It is vital to make sure that your data migration and integration procedures can successfully connect to and extract data from various sources.
  • Data transformation: The formats, structures, and quality levels of data from various sources might vary. Strong data transformation capabilities should be incorporated into data integration and migration procedures in order to clean, verify, and transform data into a standardised format appropriate for analysis. Data enrichment, data aggregation, data profiling, as well as other methods of data cleaning and validation, may be used in this.
  • Data Mapping and Integration Logic: A crucial stage in data integration and migration for business intelligence and data analytics is mapping data from many sources to a common data model. To guarantee data correctness and consistency, it entails defining the connections between data items, creating data mappings and using integration logic.
Business Intelligence and Data Analytics
  • Data Quality and Data Governance: In order to ensure that the data utilised for analysis is correct, dependable, and consistent, it is essential to establish data quality and data governance in business analytics & business intelligence solutions. To find and fix data quality concerns, data integration and migration processes should also include data quality checks, data profiling, and data validation.
  • Scalability and Performance: Processing huge amounts of data is a common task for business intelligence and analytics. Processes for data integration and migration that manage huge datasets and provide timely data availability for analysis should be scalable and performant. To maximise performance, this may entail strategies like parallel processing, data segmentation, and data caching.
  • Real-time Data Integration: As businesses need quick insights from continuously shifting data sources, real-time data integration is becoming more crucial in business intelligence and analytics. Real-time or almost real-time data from sources including streaming data, IoT devices and social media feeds should be handled through data integration and migration procedures.
  • Security and Compliance: When integrating and migrating data for business analytics & business intelligence solutions, security and compliance are crucial factors to take into account. It is crucial to ensure that data is transported and maintained securely and to follow data privacy laws like GDPR, HIPAA, and others. To secure sensitive data, data access rules, data masking and encryption should be used.
  • Metadata management: Understanding and managing data in business intelligence and analytics requires the use of metadata, which includes data definitions, data lineage, and data cataloguing. To offer visibility into the data’s source/origin, meaning and usage as well as to assist data discovery and data governance, data integration and migration procedures should capture and maintain metadata.
  • Data Validation and Testing: To ensure the correctness and dependability of data, rigorous data validation and testing should be carried out during data integration and migration operations. To find and fix data conflicts, this may entail data profiling, data validation guidelines and data reconciliation.
  • Monitoring and error handling are important to make sure that data is handled properly and on time throughout data integration and migration operations. To identify and correct data integration issues or failures, it is necessary to design robust error management and exception handling procedures.

Finally, while we conclude this topic, it should be noted that data migration and integration are essential parts of business intelligence and analytics, and care should be taken to ensure that these procedures are effective, scalable, performant, secure, and in compliance with data privacy laws. To guarantee correct and trustworthy data for analysis and reporting, it is crucial to do proper data transformation, data mapping, data quality assurance, metadata management, and monitoring.


The rise of self-service BI

Business users may access, analyse, and visualise data with the use of a variety of tools, technologies, and practises known as self-service business intelligence (BI). It gives non-technical individuals the ability for independent analytics and business intelligence and come up with insights without depending on IT teams or professional data analysts to create and distribute reports or dashboards.

Self-service BI typically involves the use of user-friendly and intuitive data visualization and analytics tools that allow business users to explore data, analytics and business intelligence, create reports, and generate insights through a self-service interface. These tools often provide drag-and-drop interfaces, pre-built templates, and interactive visualizations that enable users to easily manipulate data, create charts, and design reports without requiring coding or technical skills.

Business Intelligence and Data Analytics

Some of the key features of self-service BI include:

  • Data connectivity: To extract and analyse data for business analytics and intelligence, self-service BI applications let users connect to a variety of data sources, including databases, spreadsheets, cloud storage, and APIs.
  • Data preparation: Self-service BI applications frequently provide data preparation features that enable users to consolidate and cleanly combine data from many sources to generate a data set that is suitable for analysis.
  • Data visualisation: To enable users to interactively and meaningfully visualise data, self-service BI solutions include a wide choice of visualisation options, including charts, graphs, maps, and tables.
  • Ad-hoc reporting: Self-service BI solutions let users build ad-hoc reports instantly without having to rely on pre-built reports or templates in order to analyse data in accordance with their unique requirements.
  • Dashboards: With the help of self-service BI solutions, users may build interactive dashboards that provide key performance indicators (KPIs) a comprehensive picture and let them track and monitor company data in real-time.
  • Collaboration: Self-service BI systems frequently come with collaboration capabilities that let users share, work on, and talk about findings with team members, helping to promote a data-driven culture inside the company.

Benefits of self-service BI include:

  • Quicker judgement: Self-service Business users are given the ability for analytics and business intelligence in real-time through BI, which promotes quicker decision-making and agility in the face of shifting business requirements.
  • Less reliance on IT: Self-service BI enables business users to do data analysis, and analytics and business intelligence tasks independently and self-sufficiently by reducing the need for reports and insights to be produced by IT or data specialists.
  • Improved data literacy: Self-service BI enables business users to interact with data and acquire data literacy skills, resulting in improved understanding and interpretation of data and supporting an organization-wide data-driven culture.
  • Flexibility and customization: Business users who utilise self-service BI have the ability to build reports and visualisations that are specifically catered to their needs and tastes.
  • Improved insights and discoveries: Self-service BI gives business users the freedom to independently examine data, resulting in improved insights and discoveries that conventional BI methodologies could not have made feasible.

In general, self-service BI empowers business users to become more data-driven, independent, and quick in their business analytics and intelligence, and decision-making processes, improving business results and giving them a competitive edge.

Business Intelligence and Data Analytics, on the Cloud

Yes, a major development in Business intelligence and analytics is the growing usage of cloud-based Business Intelligence  tools. When Business Intelligence tools and technologies are used on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and others, where data and analytics resources are stored and processed in the cloud, this is referred to as cloud-based business analytics & business intelligence solutions.

Business Intelligence and Data Analytics

The popularity of cloud-based business intelligence and analytics is rising for a number of reasons, including :

  • Scalability: Organisations may scale up or down their analytics capabilities in accordance with their demands using cloud-based business intelligence and analytics without having to invest in new infrastructure. This gives businesses the adaptability to manage altering customer demands and data volume, making it simpler to meet corporate development or shifting requirements.
  • Flexibility: Cloud-based business analytics & business intelligence solutions allows organisations to quickly connect to a variety of data sources, including on-premises, cloud-based, and external data sources, for analysis, giving it greater flexibility in terms of data sources. This makes it possible for businesses to use data from many sources and get insights from various data sets for business analytics and intelligence.
  • Cost savings: Cloud-based business intelligence and analytics does not require initial hardware, software or infrastructure expenditures or recurring maintenance expenses. When compared to typical on-premises BI installations, organisations may save money by using a subscription-based or usage-based payment model to pay for cloud-based business intelligence.
  • Ease of deployment and management: Since there are no complicated installs or setups required, cloud-based business intelligence and analytics are often quicker and easier to adopt than on-premises systems. Additionally, cloud-based business intelligence and analytics frees organisations to concentrate on their core business operations by offloading the responsibility for maintaining hardware, software, and infrastructure maintenance to the cloud service provider.
  • Collaboration and accessibility: Cloud-based business intelligence and analytics enables people to view and analyse data using any internet-connected device at any time and from any location. This encourages cooperation and makes it possible for distributed and remote teams to collaborate easily, accelerating decision-making and enhancing organisational agility.
  • Advanced analytics capabilities: Built-in advanced analytics features, such machine learning, artificial intelligence, and natural language processing, are frequently found in cloud-based business intelligence and analytics platforms. These features may aid organisations in gaining deeper insights from their data and maximising its value.

To guarantee that their data is secure and complies with legal requirements, organisations must carefully evaluate data security, privacy, and compliance needs before using cloud-based business intelligence and analytics.

Overall, the adoption of cloud-based business intelligence and analytics is growing because it provides businesses with advantages including scalability, flexibility, cost savings, simplicity of deployment and maintenance, accessibility, collaboration and sophisticated analytical capabilities, making it an important trend in the business intelligence and analytics industry.

Augmented Analytics and BI

A key development in the realm of business intelligence and analytics is the introduction of augmented analytics. Using cutting-edge technology, such as machine learning and artificial intelligence (AI), to automate and improve several steps of the analytics process, such as data preparation, data analysis, and insight creation, is known as augmented analytics.

Here are some significant developments in augmented analytics:

Business Intelligence and Data Analytics
  • Automated data preparation: Augmented analytics uses machine learning algorithms to automate data cleansing, integration, and transformation. In addition to lowering the possibility of human mistake, this assists organisations in saving time and effort while preparing data for business analytics and intelligence.
  • Advanced data analysis: Augmented analytics automatically analyses data and reveals hidden patterns, trends, and insights using AI-powered algorithms. This can assist businesses with extracting valuable insights from vast, complicated datasets that may be challenging to manual business analytics and intelligence.
  • Natural language processing (NLP): NLP capabilities are frequently included in augmented analytics, allowing users to engage with data using natural language commands or queries. Because of this, business users who might lack technical competence can more easily ask questions and get answers on data analysis in a form that makes more sense to them.
  • Automated insight generation: Based on the analysed data, augmented analytics may produce insights and suggestions automatically. As a result, organisations are able to make decisions more rapidly and achieve better business results by swiftly identifying critical discoveries and useful insights.
  • Predictive and prescriptive analytics: Augmented analytics may use machine learning algorithms to do predictive and prescriptive analytics, allowing businesses to obtain knowledge of what will happen in the future and make data-driven decisions based on suggestions and simulations.
  • Analytics democratisation: By making sophisticated analytics capabilities available to a wider variety of users, such as business users, data analysts, and citizen data scientists, augmented analytics strives to democratise analytics. By enabling more users to make data-informed decisions, this aids organisations in fostering a data-driven culture throughout the organisation.

By automating and enhancing conventional business analytics and intelligence processes with cutting-edge technology, augmented analytics is revolutionising how organisations approach data analysis and decision-making. The future of business intelligence and data analytics is anticipated to be shaped by this trend, which will allow businesses to get deeper insights from their data, make better decisions, and provide better business results.

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