Data Analytics is about understanding your data and using that knowledge to drive actions. It reveals the trends and outliers within the data which might be otherwise difficult to note. It is a scientific way to convert raw data into information that helps guide difficult decisions. A number of statistical tools and software are available to perform data analytics. The nature of data and the problem which needs to be solved using the insights from data guides the choice of statistical tools and techniques. Domain knowledge and expertise are also very important to interpret and apply the results obtained from analytics. Lastly, in our experience, the best data analysts are those who have the ability to dig into the data but can also layer common sense and domain knowledge into their recommendations.

Businesses are using analytics to make more informed decisions and to plan ahead. It helps businesses to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon. It helps answer the following questions: - What is happening and what will happen? - Why is it happening? - What is the best strategy to address it? Collecting large amounts of data about multiple business functions from internal and external sources is simple and easy using today’s advanced technologies. The real challenge begins, when companies struggle to infer useful insights from this data to plan for future. Using analytics businesses can improve their processes, increase profitability, reduce operating expenses and sustain the competitive edge for the longer run.

Building analytics function requires long term commitment and extensive resources. An organization has an option to seek analytical help from in-house resources or from outside analytical vendors or use both in parallel. Any organization needs to spend considerable time and money to recruit and train in-house analytical help. At times they may not possess the required know-how to recruit such specialized staff or decide on the technologies that would be best suitable for carrying out analysis. In these circumstances they rely on analytical vendors like Ronan Analytics. Such vendors can closely work with the management team to help the organization to adopt analytics. The organization has to trust and co-operate with the vendors while sharing their data and researching it to make the analytics engagement a success. Organizations can follow another model in which they build an internal team to manage their relationships with an external analytical vendor. Many analytically mature companies resort to this to supplement their internal efforts.

A typical analytics project or engagement is generally divided into the following four stages: Stage 1 - ‘Research’ where the analyst helps to identify and understand the problems and issues that the business is facing or would be encountering in the future. At this step there is significant interaction between the management team and analysts. Stage 2 - ‘Plan’ where the analyst helps decide what type of data is required, sources from which the data is to be procured, how the data needs to be prepared for use and what methods to be used for analysis. Stage 3 - ‘Execute’ where the analyst explores and analyzes data from various angles. The analysis paves way to interesting results that are shared with the management. Based on these results, strategies are formulated to tackle the problems identified in stage 1. Stage 4 - ‘Evaluate’ where the analyst measures the results of the strategies formulated and executed. This stage helps learn and revise future strategies and processes.

A strategy built using analytics is a set of simple implementable recommendations that efficiently uses the information drawn from the data. An effective and efficient strategy suggests best use of the available business resources. It helps to find solutions for some of the biggest problems faced by the company. The process followed to formulate the strategy might be complex, but the final result is actionable and useful for management.

Analytics is not for one time or special event, yet it is a continuous process. The businesses should not take their attention off analytics and plan to adopt it as a regular business function. The business has to make collecting, cleaning and analyzing data a routine and a support role to functions that do not have the capability to do so. Most businesses look towards analytics when they face a problem and think the solution lies within their data. Once businesses start appreciating the potential analytics has to solve problems, they begin to use it to take all kinds of strategic and regular business decisions.

The resources and time required for an analytics project is dependent on a number of factors. The major factors being the scope and scale of the project, readiness and availability of required data, understanding of the analysis tools, skills and knowledge of the analytical team and most importantly, acceptance and approval from the management team to carry on the analytics project. The analytics team generally defines a project timeline dependent on the factors listed above. Intermediary findings and analysis difficulties might alter the goals and objectives of the project. This might require the team to re-work the time and resources required for completing the project. Ronan Analytics would be happy to provide you an estimate of the resources required to complete the analytics project and goals that you have in mind for your organization. Please contact us with details of your project.

Data is the most important resource for any analytics project hence the business should make sure that it captures its business and customer data in a structured manner. This will ensure that company has all the relevant data in the most usable form and can help the project move along quickly. Delays in analytics projects generally take place when the data rendered to the analytical team is not usable in its current form. The data needs to be structured, cleaned and mined to make it usable. This step can take from hours to days to months depending upon the size and form of data. Ronan Analytics would be happy to talk to you more about the state of your data and more specifically how 'ready' it is for analysis projects. Please contact us with details of your project.

For analytical needs, an organization can decide to use data analysis software like SAS or SPSS, seek help from custom consulting companies like Ronan Analytics or even build data analytic capabilities in-house. Today companies are even using a combination of the above. Each of the above options comes with their own pros and cons. An organization has to find which option would suit their analytical needs best depending upon the nature of their business and existing resources. The costs associated with these options are rarely same for any two organizations.

There are two types of models, predictive and descriptive. Descriptive models are good to explain what has happened and what is happening. Predictive models explain what would be happening and why. These models are increasingly being utilized to solve problems across finance, marketing, human resource, operations and other business functions. At Ronan Analytics, we have seen these models being used in financial services, retail, telecom, insurance, healthcare and even manufacturing industries. Increased competition has expanded the scope, the need and the use of predictive modeling. Businesses need to be more proactive than before to build or sustain a competitive advantage. They need to get answers for tomorrow even before it arrives. Predictive models are created using past and present data to foresee happenings in future. These models are being built to find answers to some of the most challenging businesses questions. It helps to manage portfolio returns, retain customers, undertake cross-selling activities, organize direct marketing campaigns, assess employee attrition and absenteeism, manage risks and formulate underwriting criteria, predict inactive customer accounts, cope with customer service requests, plan inventory and much more.

The efficient flow of data from one location to the other — from a SaaS application to a data warehouse, for example — is one of the most critical operations in today’s data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.

You may commonly hear the terms ETL and data pipeline used interchangeably. ETL stands for Extract, Transform, and Load. ETL systems extract data from one system, transform the data and load the data into a database or data warehouse. Legacy ETL pipelines typically run in batches, meaning that the data is moved in one large chunk at a specific time to the target system. Typically, this occurs in regular scheduled intervals; for example, you might configure the batches to run at 12:30 a.m. every day when the system traffic is low. By contrast, "data pipeline" is a broader term that encompasses ETL as a subset. It refers to a system for moving data from one system to another. The data may or may not be transformed, and it may be processed in real time (or streaming) instead of batches. When the data is streamed, it is processed in a continuous flow which is useful for data that needs constant updating, such as a data from a sensor monitoring traffic. In addition, the data may not be loaded to a database or data warehouse. It might be loaded to any number of targets, such as an AWS bucket or a data lake, or it might even trigger a webhook on another system to kick off a specific business process.

While a data pipeline is not a necessity for every business, this technology is especially helpful for those that: - Generate, rely on, or store large amounts or multiple sources of data - Maintain siloed data sources - Require real-time or highly sophisticated data analysis - Store data in the cloud As you scan the list above, most of the companies you interface with on a daily basis — and probably your own — would benefit from a data pipeline.

There are a number of different data pipeline solutions available, and each is well-suited to different purposes. For example, you might want to use cloud-native tools if you are attempting to migrate your data to the cloud. The following list shows the most popular types of pipelines available. Note that these systems are not mutually exclusive. You might have a data pipeline that is optimized for both cloud and real-time, for example. - Batch. Batch processing is most useful for when you want to move large volumes of data at a regular interval, and you do not need to move data in real time. For example, it might be useful for integrating your Marketing data into a larger system for analysis. - Real-time. These tools are optimized to process data in real time. Real-time is useful when you are processing data from a streaming source, such as the data from financial markets or telemetry from connected devices. - Cloud native. These tools are optimized to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, allowing you to save money on infrastructure and expert resources because you can rely on the infrastructure and expertise of the vendor hosting your pipeline. - Open source. These tools are most useful when you need a low-cost alternative to a commercial vendor and you have the expertise to develop or extend the tool for your purposes. Open source tools are often cheaper than their commercial counterparts, but require expertise to use the functionality because the underlying technology is publicly available and meant to be modified or extended by users. Ronan Analytics is the leading provider of data pipelines. If you’re ready to learn more about how Ronan Analytics can help you solve your biggest data collection, extraction, transformation, and transportation challenges, please contact us.