You hear the buzz words big-data, machine learning, artificial intelligence, etc., very often, ever wondered how it can benefit your organization?
Here are few key business problems that we have solved for many fortune-500 organizations with the help of brilliant and innovative minds that we have and we can help you if you want to do any of the following,
- Move from descriptive and historical analytics to predictive and prescriptive forecasting and analytics
- Detect anomalies
- Recognize patterns and advanced search
- Enhance decision-making and productivity
- Improve customer service and fraud detection
- Reduce expanse and improve performance by having a better insight into financial and outcome data
Also, we can help with the transition like,
- How to bring the culture of agility and Data Governance
- How to inculcate the big data mindset, given we have been using the traditional data and files for decades
Big Data, analytics tools, artificial intelligence (AI), and machine learning (ML) enable the discovery of new insights across many different industries. These kinds of technologies help make our lives easier and help solve some of the world’s biggest problems by enhancing decision-making processes.
Who we are:
We are a team of business problem solvers using various data (small, big, structured, unstructured, batch, real-time, etc.) integration, AI, ML, analytics, and dashboards tools and technologies across multiple fortune 500 companies across all the business domains like Healthcare, Fintech & Banking, Retails & E-commerce, Social media, etc.
We focus on end to end data integration, data governance, and data distribution based on business need.
In the next section, you will find the key components to succeeding with the Big Data, AI, and ML related projects.
How we solve business problems:
Business Requirement and Architecture: Big Data, AI, and ML solutions are critical business-focused domains at the global level. Therefore, understanding the business requirements and architecting the solutions with adequate rigor and vision is a compelling capability for business stakeholders. Here is one sample high-level architecture for a fintech major.
Enterprise Data Management: It is the organization, administration, and governance of large volumes of both structured and unstructured data. The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications.
Big Data Analytics and Data Visualization: Big data analytics, artificial intelligence (AI) and machine learning (ML) based intelligence enables the discovery of new insights across many different industries. These kinds of tools help make our lives easier and help solve some of the world’s biggest problems by enhancing decision-making processes.
Using the Right process, Tools, and Technologies: One solution/ technology fit all business problems leads to the failure, this is the major reason behind the project failure. How we approach the selection of the tools and technologies is completely driven by the business problem we are trying to solve. There are zillions of big data, AI, ML tools in the market, choosing the right technology stack is the key to success.
On Big-Data projects, we adopt a data-driven solution and deliver faster and in smaller chunks. Also, we keep the business owner engaged and create continuous feedback on the solution and incorporate those.
Data Security – Threat Detection and Risk Evaluation: Data security is the collective term for all the measures and tools used to guard both the data and analytics processes from attacks, theft, or other malicious activities that could harm or negatively affect them. Specifically, in the case of big data, we need to strategize it a little different than the traditional data store, as the later one comes with the inbuilt data security features up to a great extent. There are multiple places in big-data architecture that we need to secure. Following are some of the techniques how we can think about securing the big-data infrastructure:
- secure access to the cluster – Kerberos, LDAP, etc.
- define authorization for access to the data – Apache Sentry for the hive, impala, Knox for Rest API access.
- encrypt the data – HDFS encrypt (from project Rhino), Vormetric, Gazzang.
- audit and lineage – within Hadoop there is Cloudera Navigator and Apache Falcon.