Step Forward, Embrace Big Data, Breakdown Silos
With Big Data analytics, ignorance isnât definitely bliss anymore. One of the important queries that is often asked is: if one size fits all. The answer to that is quite simple: it doesnât.
Because companies want their software application to be well-architected and scale effectively, one size can seldom fit all the needs of multiple distinct cases. Thatâs one of the reasons why so many database products and data models are modeled around the needs of the specific organization looking up to Big Data technology to solve their business problems. Therefore, in this competitive market, it is highly essential to realize faster growth by acting on real-time data for business advantages.
Monolithic one-size-fits-all databases of the olden days are behind us. Today, developers are using multiple purpose-built application databases to build highly scalable and cloud-native distributed systems. They do what they do best: they break the complex database application systems into several easy solvable pieces and then pick the best technology tools available to solve each problem. The choice of tools usually differs by use case.
For years on years the database choices were based upon old and archaic relational database models. This was so because the data, external and internal, pouring in from different sources was thought to be relational and hence relational models became prevalent. This old database model was driving all the data while it should be the other way around: data should drive the database instead. That is one of the reasons why relational database models or use cases fall way short of the actual requirements of the humongous data that todayâs relational-based model handle.
The key point here to note is that not all application data models match the relational model. Therefore, the use of purpose-built multiple databases and data models that are incorporated within the same application are needed to build highly distributed, decoupled and cloud-native applications.
When the Data is Big and infinite (Big Data that is), relational models cannot tackle the new evolving data profusion, only cloud-centric non-relational distributed models have the prowess to go beyond the limits of relational databases. With todayâs technology, it is possible to do all of that and more, and the effort required to employ the big data and extract value out of it is negligible. Weâll see in the course of this article how it works and the fact that thereâs actually no single technology that can harness big data for you, in reality, there are multiple technologies that work together to help your organization derive the most value from your big data or information.
Part of the challenge that involves breaking down data silos is that it requires specialized hardware processes. Using open-source systems such as Apache Hadoop and tools like MapReduce can help you get the solution to mine the big data effectively, but such a setup may require additional technical know-how beyond just setting up a Microsoft Access or some simpler database technology that lets you extract value out of the large pools of information.
For example, a business should not rely on deploying a simple out-of-the-box solution. The potential data breaches, lost or stolen data can be at best highly risky and at worst impossible if you were to face a situation to recover your enterprise data. At the same time, relying on multiple vendors or a one-size-fits-all solution for all your problems can leave potential vulnerabilities or you are finding that your data is falling through the cracks, so to speak. What your enterprise needs are âflexibilityâ and âcustomizationâ that can positively impact the overall success of a data protection strategy of your organization.
As the challenge of keeping enterprise data protected continues to evolve, what we have today Analytics Tools & Accelerators such as Oracle, SAS, or ETL platforms to migrate or replicate your basic SQL relational databases to Hadoop for big data ETL and data lake use cases. Among the accessible and solid distributed systems, yep, Hadoop is the most famous one. So yes, big data means walking away from your old, tried and tested Excel sheets. It also means new tools like Python, R, SQL, or even Scala, Pig, MapReduce, Hive, or C++, or platforms that allow the data analytics team to manipulate the data without code.
Big Data means bringing a lot of extra value to whatever you do: it means breaking down data silos, data management and storage, great analytics, data science, automation capabilities, algorithm development and more. More than anything though, it also means new business models and ways for organizations to become more efficient in handling their data or even reinvent themselves in the process. So look no further to go from Excel to a Big Data Analytics mindset. But first, breaking down existing data silos would be a step in the right direction.
Step Forward to Breakdown Data Silos
Embracing Big Data and analytics is the first step forward toward making a data-driven or insights-driven culture possible. The insights-driven culture is dependent upon how collaborative the organization is. In fact, to help foster that collaboration steps like role-based approach, aligning team incentives, data democratization, and willingness to work together allow each team member (or individual worker for that matter) to seamlessly work with data (both structured and unstructured data) and get relevant business insights to advance company-wide priorities.
Apart from the collaborative aspect of the data-driven world, the opportunity that Big Data Analytics provides business leaders of the organization is to extract meaningful, actionable business insights from even new emerging data. Until now technology platforms were not available but now there is â technology that can help you get meaningful information and answers to key business questions even in real-time.
Breaking data silos should be a top tier priority for companies looking to cut costs, streamline operations, and deliver superior service. Data silos are dangerous and they wonât get rid of themselves. They create disjointed customer views and provide incomplete and sometimes unreliable information. What todayâs organizations need is to unlock the true value of all your data by the use of advanced technologies and get the most out of your analytics. Implementing a holistic data analytics platform to transition from old habits to a superior solution is what you need.
A good organizational strategy for data and analytics can typically improve faster, better decision-making, stay competitive, serve up better customer experiences, cost reduction, accelerate innovation, create new products and services, and respond to crises faster.
So where to begin when you want to break down silos? For any attempt to break down silos in your organization, the starting point is to:
Earn executive level buy-in. It takes a commitment from the top to set the stage for implementing a holistic/advanced analytics solutions.
Partner with an advanced analytics vendor familiar with the entire process of modernizing legacy applications. Big Data and Analytics provider can help you make well-informed decisions about operational optimization.
Then analyze the full extent of the available structured and unstructured data.
Use of machine learning, predictive analytics, and statistical modeling can help you overcome obstacles and emerge from the analysis process with actionable measures that can simplify the transition into a superior solution.
Work With the Right Partner
Ambitious enterprises donât have to step forward alone into the world of Big Data Analytics to build user-centric distributed applications with purpose-built databases. Without the proper rendering of expertise, hands-on experience, and technical know-how, enterprises cannot uncover the real value of big data and analytics solutions.
Working with the right IT solutions vendor of Big Data consulting services can help your organization achieve all of that and more. If we were to assume that technologies are changing faster than people and people faster than organizations, then tackling these challenges becomes an immediate strategic need. Companies will have to rethink on how they can harness big data to innovate and compete in todayâs highly competitive market space. Without a doubt, growing business needs a modern big data architecture so that they can employ real-time insights from a multitude of big data types, sources and locations, and the benefits of having to have such a modern system in place are aplenty. Some of them are systems of insight fed by machine learning (ML) and AI, personalized 1:1 experiences and relationships, balanced supply chains, producing data-defined new products and services, and operational efficiencies throughout the organization.
Quite often, the next bottleneck on the way to becoming truly a digitally trans-formative champion is how to make the organization embrace the new possibilities of change. Therefore, building an insights-driven culture demands a dedicated partnership which understands your evolving data silos to deliver a modern integrated tech platform that can help your enterprise turn data-driven.
How It Works and Key Technologies
We have seen that by breaking down the barriers of data silos and embracing Big Data, we can mine data to improve the decision-making process with analytics in a wide realm of activities. Now let us see what are the latest tools & technologies available in the market that you can use to harness data and extract value from it.
No single technology encompasses big data analytics. In reality, several types of technology work in combination to help you extract value from the information held in your various systems. Here are examples of the bigger players that can help you tap into the full power of your data:
Machine Learning. It is a specific subset of AI that automates analytical model building. ML can train a machine on how to learn from data, identify patterns and make decisions with minimal human intervention, quickly and automatically. By building precise models, it can deliver faster and more accurate results, identify profitable opportunities and can be programmed to perform specific tasks to produce results that are reliable, and make repeatable decisions too.
Data mining. Data mining is one of the useful technological tools to examine large amounts of data drawn in from a variety of internal and external sources or within millions or billions of records. It is a cornerstone of analytics technology that helps to find anomalies, discover patterns and correlations within large data sets to analyze further to help answer complex business questions and predict outcomes. With data mining software and by using a broad range of techniques, a data mining expert can sift through all the chaotic and repetitive noise in data and analyze & understand whatâs relevant and present analyzed data in easily understandable forms such as graphs. Data miners use sophisticated mathematical algorithms to uncover new insights, evaluate possible outcomes and then fast-track the pace of making informed decisions.
Hadoop. Apache Hadoop is one of the best open-source software platforms available in the market today. Its popular distributed computing model processes big data fast. With data volumes and increasing varieties, especially from the Internet of Things (IoT) and social media, Hadoopâs ability to store, run applications on clusters of commodity hardware and process huge amounts of any kind of data, quickly is a stuff of legend. Simply by adding nodes to your system (more the better), you can use its free open-source software platform to store large quantities of data, at a low cost.
Data management. In todayâs day and age, data is undoubtedly the most valuable resource for any organization. It pours into organizations from scanners; inbound and outbound customer contact points; web and mobile media, including operational and transactional systems, etc. Data quality is important and should be well-governed before it can go reliably used or analyzed for decision-making and a host of other organizational purposes. To be able to do that, it is important to establish a master data management program so that the quality of data is permanently ensured and gets the entire organization on the same page.
Predictive analytics. By using statistical algorithms, machine-learning techniques, and the use of data, predictive analytics can help solve difficult problems and uncover new opportunities. Predictive analytics enables organizations to identify the likelihood of future outcomes based on historical data. With predictive analytics, you can discover insights about the future. Fraud detection, cross-sell opportunities, risk, operations, and marketing are just a few common applications of predictive analytics in todayâs world.
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