Big Data
Case Studies please contact us
Core capability in Bigdata Space
- Spark / Hadoop / Parquet based Data Lakes.
- Convert Legacy systems(Ex. Informatica, Data Stage based ETL) onto Spark/Hadoop based systems
- AWS EMR, Cloudera Hadoop and Hortonworks Hadoop setup and maintenance.
- Data Science support using open tools like Zeppelin.
- End to End Cloud Support(AWS).
- Port in-house systems onto Cloud.
Industries best Practices.
The term Big Data is not just technical but is also a strategy. As an enterprise, you need to approach Big Data solutions from both IT and business perspective to gain maximum benefits. There is no single set of defined approach that will make the Big Data analytics successful for your business initiatives. You just need to follow a certain set of framework and best practices laid out by Data Lakes Solutions to help your business. Analyzing initiatives from the business and technology perspective, 8 best practices have been laid out for our clients.
1. Defining the strategy of Big Data Analytics
For an enterprise, implementing Big Data business strategy is just like investing in some new technology. Whatever the enterprise is trying to achieve whether it is to increase the operational efficiency, improve product pricing decision, enhance marketing campaign, make stronger interactions with customers, assurance of revenue, counter the risk of fraud, or manage data from the smart meters, the enterprise must be able to clearly present the situation that they want the analytics to solve. Identification of the business case leads to designing roadmap and planning Big Data strategy.
2. Connecting the Stakeholders
It is very important for the senior management to get involved actively in the Big Data initiatives for the analytical projects to be successful. The cross functional team needs to ensure that, to meet the business goals, the business needs are to be understood and the data is to be made accessible. This is possible only with the sponsorship of executive level to the Big Data Initiative. Eventually, it is the business users who get impacted with the initiatives. So it is pivotal for them to get involved right from the requirements gatherings to the execution. The role of the data scientist becomes very crucial because he is the one who creates the analytical queries and algorithms with his quantitative and statistical knowledge to extract the insights from the industry and operational specific marketplace data and from the complex customer. A data scientist should possess good analytical and modeling skills apart from strong business acumen and ability to influence the senior management with the insights and the findings that address the challenges in the business.
3. Critical Success Factors Establishment
An analytical project is deemed to be successful when it brings value to the business. That’s the reason why every analytical project must commence only after establishing CSF-Critical Success Factors. These factors are established by specifically focusing on two areas;
- . Usage of resulting intelligence by the employee
- . key performance indicators on which the analytics will be used
While the critical success factors are measured from the project management perspective, the Big Data Analytics initiatives which are key for the process will be measured in two different areas;
1.How good the business users will be able to adapt the resulting analytics intelligence: this measurement gives the information about the amount of total information consumed by the business user in the form of who is using the data, how much they are using and for what are they using it?
2.Business process key performance indicators where analytics read measurements of established metrics like customer satisfaction, efficiency of supply chain and others
When the analytics are applied by the enterprise in this way, huge ROI is delivered which helps the business organization to identify the issues before hand and fix it immediately. Once the Big Data Analytics fits into the operational routines, it can successfully get adapted throughout the enterprise giving an advantage of competitive edge through analytics.
4.Pilot Project
Running a successful POC validates Big Data Strategy and infrastructure requirements & sizing, improves the confidence of the executive sponsor, and confirms project approach thus helping the project to gain momentum. Running a pilot project will help the management to take decisions like where to make their next incremental investments before taking up larger commitments.
Whether it is to generate insights or enhance the loyalty of the customer, improve profit expansion or have a competitive edge whatever may be the objective, by executing a pilot, the enterprise can show the value of the Big Data business case and estimate the success to a much wider range.
The aim of the Big Data should be to combine internal data from the log files, data warehouses, transactional systems like log files with external data, ERP, CRM, and benchmarks. Data models collect data to reveal new patterns and relationships that provide a business case.
Technological Perspective
Technology is irrelevant for the business users. They do not care if the technology used is an in memory analytics or a graph database. What they need is being able to analyze large amounts of data for getting better insights in to the business.
Architects, just by looking at the Big Data business case should be able to assess the changes required for the data, tools and the technical architecture required and they should also be able to draft a robust and scalable plan.
Points to be considered from the technological perspective:
5. Data Requirements Evaluation
It is important to have a technical assessment of the data requirements before deciding on the tools so as to understand,
- • What is the structure of the data source (structured, unstructured or highly structured) that needs to be influential for the business?
- • What is the data frequency (on-demand? Continuous feed, real time)
- • What type of analysis needs to be used? (Batch or steaming)
- • Types of data sources that needs to be worked on (machine generated, web & social, biometric, human generated, transactional system or others)
- • Volume of the data received ( small, massive chunks or fast chunks)
- • Who is the consumer for this data? (enterprise applications, business processes, human or other repositories)
- • How the data will be stored and processed?
- • How the result s will be visualized
If the requirements are chalked out in advance, it becomes easy for the enterprise to select the best fit tools and technologies for the Big Data Analytics engagement.
About Datalake Solutions
DataLake Solutions is a solution oriented firm indulged in serving organizations with work optimizing products and services since Jan ,2016 in India, based in USA. Data-lake has an experienced and dedicated team equipped with knowledge, skills and expertise of modern era to fulfil business reqments and same is rewarded with customer’s delight.
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