data strategy

Data strategy is a plan that describes a way to use your data, which data to use, what technology you need, and how to organize yourself to get the most value out of this data, given your specific business objectives. We recommend following steps to prepare an effective strategy:

  • Discuss with Stakeholders, data management team, business leaders, data engineers, data scientists and other departments to obtain a high-level view of how to build a culture to enable data-driven decision making in the organization
  • Figure out where and in what formats your data resides and begin the process of aligning data sources with the objectives laid out. Identify frameworks for data architecture, data storage, and data operations; assess the data life cycle.
  • Identify and prioritize the use cases by starting small, thinking big and iterating often. Assess how data governance ensures that data stays reliable and secure; develop an effective approach to data security; and understand the best practices to comply with privacy regulations, such as GDPR, HIPAA and CCPA.
  • Organize data technologies into categories such as: data generation and acquisition; data integration and management; data analysis; and data operationalization.
  • Formulate a roadmap with a focus on identifying the gaps you have around the data architecture, technology, tools, processes and the skillsets. Look to the future of data, discovering what’s possible in data science, AI, and different analytic approaches that can inform your business decisions.

Business Intelligence

Business intelligence platforms are essential tools for analysts and managers who need to make quick and accurate decisions. Ida helps organisations implementing a Business Intelligence setup and recommends following steps:

  • Choose a Business Intelligence type by assessing which data you need in order to enhance your business operations and which tasks should it resolve.
  • Form a Business Intelligence team who can and will support its work and maintenance.
  • Create a Business Intelligence strategy to start considering which company data is actually required for the flow of your Business Intelligence system, what kind of dashboard do you need, and which reporting type you prefer – traditional BI or self-serving.
  • Setup a Datawarehouse that allows the BI tools to connect to the heterogeneous data from different sources of an organisation and present it all in a single convenient dashboard interface.
  • Configure data integration tools to extract, transform and load that data by choosing the appropriate ETL tools. ETL tools are used to extract and process your raw data from the original data sources and only then will forward it to a data warehouse.
  • Develop BI Dashboards that helps business users make better-informed decisions by letting them gather, consolidate, and analyze and visualize it in a meaningful way. They aim at simplifying a complex analysis of huge amounts of information, to avoid missing any trend or pattern.
  • Train your employees by conducting training sessions for all employees – welcoming them to an idea of BI and how their daily duties will interact with it.

data engineering

Data Engineering is the field associated with analysis and tasks to get and store the data from other sources. Then, process those data and convert them into clean data used in further processes such as Data Visualisations, Business Analytics, Data Science solutions, etc. Data engineering team plays a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics.

We recommend following steps to achieve an efficient data engineering:

  • Building data pipelines and efficiently storing data for tools that need to query the data.
  • Analysing the data, ensuring it adheres to data governance rules and regulations.
  • Understanding the pros and cons of data storage and query options
  • Data Acquisition: Finding all the different data sets around the business
  • Data Cleansing: Finding and cleaning any errors in the data
  • Data Conversion: Giving all the data a common format
  • Data Disambiguation: Interpreting data that could be interpreted in multiple ways
  • Data Deduplication: Removing duplicate copies of data

Once this is done, data may be stored in a central repository such as a data lake or data lakehouse.

data analytics

The big data transformation has brought forth various types, and phases of data analysis. The key to organizations effectively utilizing Big Data, is by picking up the correct data which conveys knowledge, that enables organizations to gain a serious edge. There are four types of analytics available that are widely used in the data space.

  • Why did it happen ?
    The benefits of Diagnostic analytics include a better understanding of your data and various ways to find the answers to company questions. This type of analytics enables businesses to understand their customers by using tools for searching, filtering, and comparing the data produced by individuals.
  • What happened ?
    Descriptive analytics is one of the most common forms of analytics that companies use to understand their current business situation better in comparison to the past
    and the company’s operational performances.
    One of its main benefits, however, is that it helps companies make sense of the large amounts of raw data they gather by focusing on the more critical areas to make data-driven decisions.
  • How can we make it happen?
    Prescriptive analytics takes the results from descriptive and predictive analysis and finds solutions for optimizing business practices through various simulations and techniques. It uses the insight from data to suggest what the best step forward would be for the company.
  • What will happen?
    Predictive Analytics is about making predictions about future outcomes based on insight from data. In order to get the best results, it uses many sophisticated predictive tools and models such as machine learning and statistical modelling.

application engineering

The primary role of Application Engineering is to design and improve software by performing needed evaluations with clients to understand the unique goals of each project and then implement after careful assessment.

Understand what the client is trying to accomplish and make the best recommendation with significant experience in following areas:

  • Agile Adoption and Transformation to help the organization perform well
  • in a flexible, collaborative, self-organizing, fast changing environment.
  • Application management helps the organization maintain, enhance and manage custom applications and packaged software applications.
  • Application Modernization to take your application environment from current state and transforming to a more agile, elastic and highly available state of operation.
  • Architecture
  • DevOps by guiding you o realise full value of the DevOps and achieve CI/CD with machine data
  • Automation consulting and implementation
  • Quality Engineering by providing a comprehensive QE solutions to
  • help ensure your team is delivering the highest quality digital offerings and ensuring maximum test coverage and quality.