Tame your Data now!
Manage your way (and data) to success
Due to the evolution of information technology, it is now possible to analyze large amounts of data with mathematical techniques. Some of these existed decades ago, but not used due to limitations in storage and processing capacity.
Nevertheless, the name given to data analysis has changed: Business Intelligence, Data Mining, DataAnalytics, Big Data, etc., each with different scope, but with the same objective: starting from data to understand what happened and why, to have an idea of what could happen and, if possible, obtain a recommendation of the best course of action.
Next, we delve into the advantages of managing data, what are its sources, how are the analysis process, and the benefit of implementing this process in a cloud platform.
Advantages of managing the organization’s data
1. Proactivity and anticipation of needs
When sharing data with organizations, clients expect them to recognize them, generate relevant interactions, and provide adequate experience at all points of contact. By understanding the needs of customers, organizations can optimize the customer experience and develop long-term relationships.
2. Delivery of relevant products
The collection of data, combined with analysis, helps companies remain competitive when demand changes and new technologies developed. It also helps them anticipate market demands to provide the product before it is requested.
3. Personalization and service
Companies must be extremely receptive to deal with the volatility created by the information collected from customers. Being able to react in real-time and making the client feel valued is only possible through advanced analysis. Data analytics offers the opportunity for personalized interactions with the client.
4. Optimization and improvement of operational efficiency
The analysis of data to design, control the process and optimize business operations, guarantee efficiency and effectiveness to meet customer expectations and achieve operational excellence. Companies can use advanced analysis techniques to improve field operations, productivity, and effectiveness, as well as optimize the workforce of the organization according to business needs and customer demand.
5. Risk mitigation and fraud
Security and fraud analysis aims to protect all physical, financial, and intellectual assets from misuse by internal and external threats. The ability to analyze data offers optimum levels of prevention. Data management, together with timely and transparent notification of fraudulent incidents, results in improved fraud risk management processes. Besides, the integration and correlation of data throughout the company can offer a unified view of fraud in various lines of business, products, and transactions.
The process of analyzing the data
Data Analytics covers the whole cycle of data management, from the collection of raw data, its storage, organization, processing, analysis, and visualization in the form of a report or control panel so that they can be used to make decisions.
Data Analytics is a multi-stage process where multiple actors, both internal and external, are participating. External participation is frequent in practically all stages except in the decision making of the organization.
Advantages of Cloud as a Platform for Data Analytics
1. Costs
One of the main advantages of using a cloud is that it avoids the cost of acquisition.
The cost of operation can also be reduced, by lowering servers or resources in periods of lower load and using serverless services, paid only when used.
2. Implementation time
Another advantage of the cloud is to be able to have a platform almost immediately, in many cases preconfigured, minimizing administrative and logistic delays.
3. Security
The platforms in the cloud have multiple data redundancy mechanisms that allow detecting and correcting errors, reducing the probability of failure in both the transfer and storage of data.
4. Centralized data architecture
It facilitates the creation of an environment for multiple users using different analysis tools on a shared data set.
Reduce the cost and improve the governability of the data compared with traditional solutions that require multiple copies in different platforms.
5. Decoupling of storage and processing
The decoupling of handling from storing allows you to process and analyze the same data with a variety of tools.
You can optimize the platform to provide the right CPU, memory, and bandwidth capabilities for better performance.
6. Integration without a dedicated server
Using serverless services to execute code without the need to provision or administer servers, you only pay for the actual amount of data you process or the calculation time you consume.
7. Standardized application programming interfaces (API)
REST APIs are programming interfaces that can be used to interact with data stored in the cloud from various analysis tools. It allows users to use the tools they know best and feel more comfortable to perform the data analysis.
The Limitations of a Big Data Platform in the Cloud
With all the advantages of using a platform in the cloud, there might be cases where it is necessary to use a dedicated platform (on-premises). It might happen when there are restrictions, either by internal policies or by government regulations, to get the data out of the organization or from the country (e.g., medical records).
Another case that may require a dedicated platform is when, considering the available bandwidth, it becomes infeasible to upload the data to the cloud due to its large volume and frequent updates.
Finally, a situation that must be analyzed occurs when, despite not having regulatory or bandwidth problems, the latency in the communication is too high due to the hops in the link, especially if you are waiting for a response to act.
However, the gap of these limitations is closing and relevant only in very particular cases.
The Getty/IO Way for Big Data and Data Science in Production
On the projects we are currently helping customers build, we’ve found ourselves using a lot of the following tools and stacks:
- Python programming language for analysis on large datasets, artificial intelligence, and scientific calculation.
- AWS Lambda and Serverless to keep costs to a minimum. Step functions to trigger chains of events
- AWS S3 buckets for low-cost yet very flexible storage and retrieval of all types of data
- AWS Redshift for a scalable and straightforward warehouse to store, process, and query large or complex datasets. Easy to set up (in minutes), high performance yet very cost-effective
- AWS EMR, Spark and Flink to process vast amounts of data quickly, and cost-effectively at scale
- AWS ELK Open Distro for real-time data analytics and pipelines
- AWS API GW to integrate all current and legacy APIs, and other data sources quickly.
Final thoughts
With the right mix of technology and a highly capable team, it is now possible to unlock the full potential of the data for beneficial business results. For example, the ability to use data to understand the customer’s path better is imperative to create a better customer experience. Possibilities are endless.
Please comment below and share your thoughts and strategies to manage your company’s data.
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GETTY/IO is South America’s largest remote consulting firm, specializing in devops, data science and blockchain technologies.
We offer comprehensive technology solutions that are tailored to fit the unique case of each project. If you have ideas, We have the team and strategy to implement them. With decades of experience in designing and creating outstanding applications, we’ll go the extra mile to bring you the most cost-effective solutions for your tech-related needs.
Say hello at https://getty.io. We are present in the United States, Canada, Brazil, Chile, Peru, Belgium, Colombia and a hangout away from anywhere on the planet :)
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