InnoBoon transforms businesses around the world through products in data and video analytics, and services for product engineering, cloud transformation, data analytics.

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Data Migration
Innoboon_data_migration

How a fashion retailer achieved massive growth through Data Migration

Case Study – 14-year-old apparel company expands rapidly A lifestyle apparel brand established in 2008, known for their premium clothing and accessories has expanded across the US and 30 other countries. From 2016, the then 8-year-old company has grown a phenomenal 400% in 6 years. This is what the CEO has to say. “We can look at our reports during the day and identify our weaknesses, and respond in a way to turn those back to the growth path. We need those dashboards! Data migration enabled us to scale our business.” Their disparate data was spread across different systems and silos, in various formats. The data processing could not provide timely, accurate reports. The DWH Advantage: ⦁ Discovery: Assess, identify all the data, and the current and future requirements⦁ Extract: Extract all the data. Design and build a new data warehouse, suited to the data and to the requirements.⦁ Clean and load all the data into a Single Source of Truth (SSOT). Provide access to all stakeholders across the organization. Build queries, reports, dashboards for fast and easy use. Data Pipeline and Data Platform Benefits ⦁ Both structured and unstructured data can be accessed and processed⦁ Aggregate data from multiple, disparate sources⦁ Data streaming and shorter processing time provides superfast results⦁ Scale your data volume up; Enable managing humongous amounts of data⦁ Significant savings – data is processed a lot faster, and the storage and processing services are cost effective Do you want to find out how Data Engineering can bring immense value to your business? Call us. Where’s the magic? #dataengineering #datamigration #etlframework #datawarehouse #ssot #dataanalytics #fastquery #dashboards #costsaving #fashionretail #apparel

Technology
Innoboon_digital_banking

Are banking customers delighted or distraught?

Reasons why most banks are lacking in providing a good customer experience: ⦁ Today’s customers are looking for better, more personal experiences when they deal with their banks. However, banks rarely tend to talk to their customers. Gone are the days when customers knew the staff, tellers and managers in their banks.⦁ Most banks are business entities with a large number of customers; they do not recognize their customers’ individual preferences.⦁ Banks do not segment their customers based on their varied interactions. Most banks believe all humans interact and respond in very similar ways and tend to offer standard services across their customer base regardless of their personality or financial situation.⦁ Finance is an important aspect in everybody’s lives. But the main institution that handles a person’s finances does not provide useful information at the right time, which can help customers make better financial decisions and feel comfortable.⦁ Like most large institutions, banks too tend to move slowly, given the volume of data they handle. Most often banks deal with their high-net worth customers with special attention, but tend to be barely helpful when dealing with others. Is there a way out of this quagmire? If banks need to attract customers and keep them, they need to offer experiences that are better than what is on offer now. However, looking at this situation from a bank’s perspective could explain why this is so. Handling vast volumes of data, including details of all transactions, customers’ demographic data, general and specific economic data, and rules and regulations, can be overwhelming. A way out is to segregate a bank’s functions into small modules and work with smaller-sized problems in a deliberate manner, yet connect each function’s data intelligently. Current advances in data engineering and analytics, high-efficiency infrastructure, and relatively easy access to technology can enable banks to up their service standards. What can be improved? Easing the onboarding process – account opening and introduction to other products and services Proactively providing personalized information about: ⦁ products and services⦁ terms and conditions both internal and compliance-related⦁ status of accounts held by the customer⦁ privacy, security, and anti-fraud information⦁ quick and easy access to support staff⦁ intuitive and friendly automated support system⦁ aggregate customer feedback and proactively address concerns and complaints Where’s the magic? The magic lies in digital transformation. We at InnoBoon, have extensive capabilities in extracting data from disparate sources, transforming the data in a homogenous set, loading, and applying analytics to generate meaningful information about customers to all the touchpoints of a customer’s journey through their interaction with the bank. We have experts in predictive analytics to forecast customer demand. Call us to have a conversation about how technology can enhance customer experience and provide avenues to bring down costs. #fintech #fintechproducts #fintechproductdevelopment #fintectsoftwaresolutions #customerpainpoints #banking #datatransformation #dataanalytics #predictive analytics

Technology
innoboon_data_engineering

5 Steps to Creating Business Value using Data Engineering

Introduction In the contemporary business world, data represents an untapped source of value. In almost every industry, data is produced from a variety of internal and external sources. Data engineering is rapidly evolving from a business option to a necessity in your company. Business operations generate a growing amount of data, which may be utilized to open new revenue sources and reveal fresh information about your consumers and their behavior. To implement the techniques and procedures that are part of a data engineering project and add value to your customers and your business, our data engineering team will need to understand how you view your data from a business viewpoint. Data discovery and interpreting Data discovery is the process of exploring your existing data to see what insights it contains. By creating a data catalog, data engineering allows you to gain a better understanding of your data. A data catalog is an organized inventory of your data that allows you to understand what data exists and how it is formatted within your company. A data catalog enables your team to quickly find a specific dataset or file and use the information to optimize operations, create new products or services. Data engineering also aids in data comprehension by generating data dictionaries that explain the structure of datasets. The entire process of aggregating data from disparate sources and updating it into a data warehouse employs an “ETL” framework, which stands for Extract-Transform-Load. Data warehouse to aggregate data As your company begins to investigate the data it has gathered, it will recognize the potential for developing new products and services. As this occurs, the data engineering team will need to be able to access and use the data in a timely and efficient manner. Data engineers can assist in the development of a data architecture that will allow your company to quickly access the data it requires. A data warehouse or a data lake enables easy and seamless access to data. A data warehouse is a centralized storage location for raw data. It is a system that gives you quick and easy access to your raw data. This data warehouse could be built with a mix of technologies such as data virtualization, data integration, and data migration. By understanding your organization’s needs and goals, data engineers will help you decide which of these tools is best for your business. Data engineers can also assist you in determining which data warehouse software is best suited to your needs. Data Virtualization for Live Access to Data Data virtualization is a method of storing raw data while also providing a customized user interface for accessing and using that data. Data virtualization enables you to create a database that only stores the information required by your business. Depending on your organization’s needs, this database contains all your raw data as well as filtered data. Data engineering will assist you in determining how to construct a data warehouse that houses all your raw data, while remaining user-friendly enough for your employees to access the information quickly and easily using ad hoc query methods. Data Platform to manage Data Life Cycle A data platform is a centralized tool for managing the life cycle of your data. This data platform will help you automate common data tasks like data ingestion and cleansing, as well as store your data in a centralized location. Dashboards, reports, and data feeds to other applications are created, to improve the efficacy of the stored data. A data platform is also used to train AI systems, build machine learning models, and perform predictive analytics. By understanding your company’s needs and goals, data engineers will help you decide which tools would be best for this part of your data platform. Standards and best Practices to manage data accuracy and consistency Increasing data accuracy and consistency, reducing data latency, and improving data quality are all examples of organizational efficiencies. Data engineers will assist you in increasing the accuracy and consistency of your data by implementing standards and best practices. The amount of time it takes to transfer data from one location to another is referred to as data latency. Data latency is frequently caused by your data being stored in multiple systems. Data engineers will assist you in reducing data latency by implementing centralized data storage and data ingestion methods that keep all your data in one place. The accuracy and consistency of your data are referred to as data quality. Data engineers will assist you in improving data quality by developing data quality checks for new data and implementing quality control measures to ensure that your data is accurate and consistent. Summary of the engagement ⦁ Discover and interpret⦁ Build an ETL Framework – collect all data into a data warehouse of a data lake⦁ Cleanse or scrub the data of inaccurate and unnecessary data; find relationship across the entire data set⦁ Analyze how your business uses data⦁ Build dashboards, reports and data pipelines to other applications and business entities⦁ Further analyze data to find deeper insights in the stored data. Use artificial intelligence and machine learning technologies, predictive analytics. Conclusion Data engineering is a critical role in any company that relies on data to run its operations. Data engineers must understand how your business views data to create a centralized data architecture that allows your company to quickly access and use data most efficiently. How? InnoBoon has a track record of implementing data engineering projects across multiple domains, and geographies. We use tools like Redshift, Snowflake, Airflow, Spark, Kafka, and others. Call us. Let’s have a conversation on how best your data can be engineered to extract tangible value for your organization.