It’s crucial for an organization to be prepared when working on a big data project. Here’s how you can lay down a roadmap with fixed goals to achieve the desired results.
Big data and data science are one of the most exciting areas in the world of business today. The key decision makers in an organization have realized the importance of these two areas, yet organizations remain skeptical on how to implement sound big data strategies. Big data and data science can be incorporated in multiple ways. Here are a few practices to help you chalk out the perfect big data implementation strategy for your organization.
Big Data Analytics Strategy – The Outline
The first step in big data implementation would be to ensure a strategy which synchronizes with the core business objectives. The strategy may be implemented to solve a multitude of problems like betterment of operational efficiency, predicting consumer behavior, fraud analytics for risk mitigation and many more.
The business strategy should be in sync with the following points for effective problem solution:
- The entire business strategy must be in sync with the organization quality and performance goals.
- Measurable outcomes should be the prime focus.
- The growth of the company should be based on data-driven decision making.
Making the Right Choice
We see a tremendous amount of data being generated today. Companies find it difficult to find the right kind of data to tackle their problems. For example, social media generates petabytes of data in the form of comments, messages, videos, opinions, etc. These account as unstructured data. The best solution is to determine what decisions an organization needs to make using the available data.
Following this, data acquisition will take place. This happens in two phases:
• Ingestion:
Gathering of structured data from various sources like CRM, call records, point of sales, and bring them onto a common platform. This also includes the gathering of unstructured data like logs, social media feeds, photos, etc., and bringing them onto this common platform.
• Transformation:
Once the data is acquired. It is refined, filtered and organized for analysts to analyze deeper using the right data science tools.
Another crucial factor is to choose the right big data tool. Today, many big data tools and technologies are flooding the market. They are designed to process vast amounts of structured and unstructured data easily and efficiently. These tools analyze the data further and help you gain actionable insights.
The Analytical Process Roadmap
Once you have zeroed in on the analytical/programming tools, a clear process and analytical models must be defined with the aim of establishing critical success factors. In order to enable data-driven optimization, advanced analytical models are required. If you take the case of predicting consumer behavior online or in stores, it will need a huge amount of past data.
This, in turn, will require an analytical model to resolve a wider range of optimization problems across various functions and business units. To make the process simpler, it is better to have the data filtered for the analytical model.
Cloud Operating Models Synchronization
The whole analytical process should be set up collaboratively within the organization. The entire flow of data from pre-positioning, integration, summarization, and analytical modeling must be in control of resource management. Private and public cloud provisions with high-end data security must be in place all through the process. The cloud-based operating gives you an advantage wherein you can scale-up based on the business requirement fluctuation.
The Pilot Project
Running a pilot project is the best way to debug the entire process. It will help your organization minimize risk before deployment. This will allow you to make the final investment with a lot more confidence.
A project with big data and data science at the center should have the capability of combining internal data from multiple sources as well as external data from social media or third-party sources. Pilot projects typically lead to bigger big data projects, and it is imperative to have the following points in mind while you define the requirements:
- Data should be easily accessible, reliable and secure.
- Data privacy and security should be built into the project.
- Contingency plans should always be in place.
- ROI measures should be captured qualitatively or quantitatively or both.
- Embed analytics into decision-making
To gain competitive advantage, the need to embed analytics into business decision making is extremely important. With big data growing at such a rapid pace, the ability to model and forecast data is becoming a regularity. Today, all important decisions are taken only after analyzing data, and advanced analytics is exactly what help companies stay ahead of the curve.
You can only consider your big data project a success if you reap business value from it. Proper governance of big data, which ensures a data-driven culture, is the key benefit obtained from big data and data science project implementation.