Taking the Steps for Your Business to Harness and Use Big Data
With the arrival of more powerful computing systems (such as cloud computing servers), the possibility and ability to capture and analyze massive amounts of data that a business deals with daily, became a reality. Big Data encompasses the large amounts of datasets that flow through a small or large business in a given period of time. Such massive data sets must be collated, parsed, analyzed and often stored, queried and transferred. Big Data helps executives to find emerging patterns and trends within an organization, allowing new business models to be developed and new marketing strategies to be adopted based on past business dealings, customer-engagements, and more. That said, the core idea of how big data can be leveraged by businesses of all sizes, is not in the volume of data, but in how the data can be used to serve the business. Big Data can help CIOs (Chief Information Officers), CDOs (Chief Data Officers) and data scientists ask and answer pertinent questions to increase the overall bottom line. Business Intelligence, provides answers by turning raw data into practical insights.
It is pressing that organizations understand how to leverage big data, and how to turn raw business data into actionable insights. As data scientists begin to supplement business analysts, data analysis is expected to continue growing. According to the Global Developer Population and Demographic Study 2017 Vol. 1 by Evans Data, as noted by Capterra, roughly 33 percent of developers worldwide (six million to be exact) are working on Big Data applications and analytics services. With that in mind, the necessary steps to harness Big Data for both small businesses and large enterprises need not be completed overnight, as corporate workflow alterations and upgrades may require some time. In fact, it may be more cost effective and efficient to integrate new technologies for Big Data use in a gradual, well thought out plan, thus allowing organizations to gradually become more data friendly.
The First Step is the Ability to Capture the Data That is Needed
Before data can be parsed and analyzed - and thus turned into actionable insights - it must be collated and stored. Thus, the primary initiative for harnessing Big Data should always be determining how to capture and collect the massive amounts of data that a business may produce on a daily basis. To do that, it is important to have a detailed understanding of the different ways that data can be collected. The two core ways include manual capturing methodologies and automated capturing methods:
- Manual Capture: Data Scientists can manually oversee, log and record specific pieces of data using data-collation software as is necessary.
- Automated Capture: Once corporate gateway systems are set up for customer engagement, programs and algorithms can use predefined parameters to collect certain types of data automatically.
To help with the collection process, it is important for executives to decide what data can and should be captured. This includes which capture methods should be used. Commonly used significant data points can include customer transactions, customer searches and queries, specific keyword loggings, data associated with customer engagements, and so on. About collection, it is important to have a CIO, CDO or data scientist design a Master Data Management policy. This will help to create processes and workflows that clearly define all data-capturing procedures and methods.
Software That Automatically Collects Data
When planning for predictive and prescriptive analytics, several choices for automated data collection exists, such as IBM’s Predictive Analytics, Microsoft’s Azure Machine Learning, and Google Cloud Prediction API. The learning parts of these offerings can be designed with organizationally specific parameters to watch, log, record, and collect business data. The data is evaluated, stored and then used to gain actionable insights and a deeper understanding of what the data means. The insights and knowledge can be utilized to target specific customer demographics and discovering what current and potential customers are really looking for in a product or service.
Software for Manually Entering Data
While it can be efficient and sometimes simple to integrate automatic data-collection programs into daily workflows, it is often necessary to use manual methods for human interaction. For example, consider hand written suggestions, notes taken on paper, manual data entry of new or updated orders, data included on engineering diagrams or quantities of inventory used during the data. Many times, adding data to CRM (Customer Relationship Manager) or ERP (Enterprise Resource Planning) software falls into this area. Thankfully, auditing business data sets using software and algorithms allows data scientists and statisticians to update, add, remove or manually enter new pieces of data that are vital to business growth.
Hardware for Collecting Data
The Internet of Things (IoT) is a concept of many interconnected devices that share data. This allows for an enterprise to collect all sorts of useful information about their business that may not be easily captured. This can include sensors on various devices like thermostats, GPS units for location-specific services (and its associated data), and smart-devices. That data can be added to central servers, customer feedback loops, and other processes. When analyzed in-depth, the insights learned can launch new strategies and business models. Integrating hardware into corporate data-collection workflows typically needs Internet connected smart-devices and a large area network, like a cellular or wireless network.
Next is the Ability to Efficiently Store and Manage the Data
After data collection, large enough storage and efficient data management strategies need to be used so Big Data analysis can be a successful part of a business’s workflows. Of course, capturing volumes of data typically needs robust computing systems and storage servers. According to an EMC report, Big Data in 2020 could mean production of 1.7 megabytes of data, per person, per second. The volume of data for a business would be a significantly larger amount. A second study from DeZyre shows Wal-Mart gathering 2.5 petabytes of unstructured data every hour by one million customer transactions.
An operational data store (ODS) supplying a Data Warehouse (DW) is one of the best ways for a business to store and manage data. An ODS and DW are central database systems specifically designed for high-performance query-based data storage and analysis. Data is added from business systems across an entire enterprise, enabling research, analysis and data mining. Additionally, since an ODS and DW are centrally stored and managed, data is more secure and access is more manageable. This type of system allows at-a-glance overview of business processes, reporting and dashboards, allowing stakeholders to plan new strategies and tweak corporate workflows. Since the data is accessible to managers, data scientists and executives, this allows for a corporate-wide sharing and co-analysis of data, all while the data is kept in a secure fashion. While relational databases are commonplace, they are designed for data storage and not analytics. Using a RDBMS for advanced analytics will require a significant boost in hardware and memory.
Big Data Needs New Databases
Traditional Relational Database Management Systems (RDBMS) such as Microsoft SQL Server and Oracle provide fast insertion of consistent data, using SQL (Structured Query Language) for data queries. They were designed for a time when storage and computing time was expensive. Adding new data needed to be fast and smaller in size, which was solved by a relational model. A relational model is one where tables hold smaller sets of data that are related in some fashion to other tables. Conversely, Big Data and DWs are designed for fast queries and data analysis, using non-relational database systems. Non-relational storage makes analysis and analytics faster and more efficient. Centralized ODS and DWs to incorporate data from different systems across an enterprise are supplemented by newer, NoSQL (“Non-relational” or “Not Only SQL”) databases to enable working with large data sets. The non-relational nature of these databases are particularly advantageous for fast results of analysis and analytics.
Another particularly useful database system that can be worthwhile for Big Data analysis is Hadoop, a framework for distributed, large data sets. Based on Google’s MapReduce model, Hadoop is a data storage and distributed processing system, specifically designed for enormous data sets. Both NoSQL and Hadoop allows an enterprise to optimally make use of DWs in order to parse, process and analyze Big Data.
The Ability to Have Executives Easily Access and Interpret the Data
Data Science is necessary to perform advanced data analysis. However, using Big Data should be easy and user-friendly, so citizen data-scientists, managers, and executives alike have the ability to use the data without much technical training or added skills. If there is a major learning curve to harness Big Data into actionable insights, improved workflows through Business Intelligence insights may not be possible. In such a situation, executives may have to rely on data scientists and analysts. To help overcome this, tools and software like Microsoft’s Power BI and Tableau are typically aimed at making analysis, assessments, and data processing commonly accessible to increase efficiency and to optimize workflows.
Dashboards and Live Tables
About those tools, Business Intelligence dashboards and live tables, used by the software, help to analyze patterns and trends in Big Data. The insights produced allow an executive to revamp workflows and produce new strategies in a quick manner. While reports alone can be used for in-depth analysis, dashboards and live tables offer real-time insights that can reveal issues - and opportunities - as they arise.
Software for Deeper Analysis
Live tables offer real-time or near real-time insights, but deep analysis needs software and programs to produce projections, predictions and in-depth reports. Statistics and insights can be quickly found using programs such as R, SAS, Stata and using common packages such as the H2O software package. The H20 package is used to handle “big datasets,” like the information compiled from transactions and customers on Amazon. Standard statistics use a subset of data, known as a population, to determine the outcome of algorithms and models. H20 is able to consume the entire set of data to provide deep-learning of the data. It can then generate visualizations and solutions for the same algorithms and models, with a more comprehensive source of data.
Big Data is a new and exciting way to analyze large corporate data-sets, made possible due to the rapid advances in computer processors, memory and storage. Significantly sized data sets cannot be efficiently analyzed with traditional data analysis software, so implementing the correct infrastructure, software and support staff is important for companies. A well designed infrastructure will allow for efficient collection, storage, cleansing, managing and analysis of volumes of data to make producing insights and new business strategies faster, feasible and profitable.