Organizations have been able to achieve 30% growth year on year after introducing data analytics and funneling in practice to setup product direction.Competitive landscape is forcing organizations to identify innovative ways to connect and serve with users.
Data Analytics have been in forefront of technology improvement. This is not new domain though, but recent focus has increased popularity of Analytics. Market leader quoting Data as new Oil and many organizations now having special teams to help them in Growth, this industry is bound to exhibit remarkable expansion at a CAGR of 30.08% during the forecast period of 2017-2023 and reach the market valuation of USD 77.64 Bn by the end of the forecast period.
Data Analytics is a technique which works with various data sets, in order to obtain conclusive patterns and trends on timeseries with support of tools. Exploratory data analysis (EDA) and confirmatory data analysis (CDA) are few methodologies used in Data Analytics. EDA is used to find the data patterns while CDA with the help of statistical techniques. Techniques such as data mining, predictive analysis, machine learning and others helps in Data Analytics, over the high volume equipment data which contains structured or unstructured or semi-structured data. Further, churn out useful information from it. Data Analytics is almost used in every sector now be it, financial sectors in back and credit card companies for preventing fraud or identifying spending patterns of customer.
Companies use various tools to identify all data patterns. There is concept of Data Lake to be created from all data available in organization. More data helps in identification of right trend and reduces error delta significantly.
Google Analytics, Omniture by Adobe are few widely used tool for analytics. In addition to these many organizations tend to create their own click stream tools to reduce dependency and cost.
Separate Dataware house strategy is built to store humongous amount of data. This is supported by various caching strategies to reduce latency. ClickHouse, Druid, Presto in combination with Hadoop provide good support for storing and running fast queries.
On top of data, various segments of users and cohort are built to analyze user trend. Segment also help in identification of strategy for A/B Testing of features. Funnels help in identification of user reach in product and drop offs.
New segment has been carved out of Growth analytics field called Funneling. This is being used specifically to identify data and action funnel and take optimization action at each stage.
Overall direction on data driven decisions is helping certain organizations to grow 5x year on year. Specialists positions are opened and expert consultancy is sought for. People involved in Data Analytics are constantly working on trends and outlook and provide best solution as per need and time.
Organizations which are not digitized or which lack in taking this opportunity might miss increased growth and user reach out. Service optimization may also get hampered and which rapidly changing demand, their product many not remain relevant.
There is great onus on leader so organization to take Data Analysis in perspective to ensure their company/unit is armed with sufficient help via external consultancy support or internal team building.
Fastcurve can help organization via its consultancy and services offerings. Our Subject Matter Experts can guide to achieve targeted milestones and run on demand campaigns.