Data is now becoming the modern currency and where companies from all the sectors aiming to gain higher value and even go-ahead to unleash the deeper insights.
Modernizing the way you store, manage and harness the data becomes the most prominent consideration. So what it requires to adapt and advance with data is systematic thinking which paves your way for long term data modernization.
A sustainable data modernization strategy requires clarity of objective a business wishes to achieve in both the short and long term.
- Understanding vision with contextual knowledge of the business, which could be either of:
- Deriving key insights for a particular business unit or division on a regular basis for efficiency
- Rethinking & revamping legacy data landscape to drive informed boardroom meetings
- Building real-time stream analytics characteristic to reflect & drive rapid business moves
- Finding ways to transform unstructured data to the systematic structure to enable a unified view of the business.
- Leveraging the power of AI and Machine Learning to unleash deeply hidden insights.
- Analyzing pain areas and obstacles from a business standpoint
- Recognizing business problem/need in a contextual manner
- Take the time to understand the current landscape and layout a strategic, multi-year migration plan.
- Finding essential data sources to shape the current perspective and develop newer ones.
- Based on the use case creating the Data pre-processing plan with appropriate techniques.
- Creating the Hadoop ecosystem to process huge amounts of data frequently.
- Data Lake creation for finding insights from the unstructured heap of data.
- Data Lake creation to build scalable ML models.
- Planning an Archival strategy for your enterprise for various dormant assets like Files, Emails or legacy applications retirement all powered with a blazing fast search engine.
- Envisioning a cloud migration for existing Databases from Data Centers.
- Analyzing the processed Data:
- It is achieved with a key phase of Exploratory Data Analysis, Data cleansing and many such important stages.
- With Data aligned in required format, extracting value and essential info. is fast, accurate and reliable.
- Methods like Descriptive, Predictive and Prescriptive analysis for BI are pretty vital.
- Visualization the values and Insights:
- Letting the Data narrate it’s own story visualizations acts just like illustrations.
- This is the stage where your data confess to everything and reveal most hidden insights.
- Data Visualization is that final touch without which a story can’t be concluded.
- Using intuitive dashboards with relevant visuals in form maps, charts, graphs filtered on time period, demographic and other essential parameters.
- Empowerment & Advancement:
While mostly this seems as the end in a standard business partnership but at 47Billion we stay with our clients in the on-going journey to support but more importantly help them embark on new adventures to explore unchartered territories with the power of data.
Have a burning question regarding how we help our clients on other aspects of Data Analytics and AI, feel free to get in touch…
