Creating a Big Data Platform Roadmap

Perficient Data & Analytics

One of the most frequently asked questions by our customers is the roadmap to deploying a Big Data Platform and becoming a truly data-driven enterprise. Just as you can’t build a house without a foundation, you can’t start down a big data path without first establishing groundwork for success. There are several key steps to prepare the organization to realize the benefits of a big data solution with both structured and unstructured data.

Big Data Ingestion: Parameters, Challenges, and Best Practices


Businesses are going through a major change where business operations are becoming predominantly data-intensive. quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. A large part of this enormous growth of data is fuelled by digital economies that rely on a multitude of processes, technologies, systems, etc. Data has grown not only in terms of size but also variety.

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Mastering Data Variety


Data variety — the middle child of the three Vs of Big Data — is in big trouble. . It’s in the critical path of enterprise data becoming an asset. And it’s been slow to benefit from the kind of technology advancements experienced by its “easier” siblings, data volume and data velocity. Meanwhile, most enterprises have unconsciously built up extreme data variety over the last 50+ years. Structured data” may sound like it’s organized.

Key Differences between a Traditional Data Warehouse and Big Data

Perficient Data & Analytics

Traditional data warehouse solutions were originally developed out of necessity. The data captured from these traditional data sources is stored in relational databases comprised of tables with rows and columns and is known as structured data. So how do you make the data gathered more useful? While Excel can be a useful tool, there are limitations and problems with the freshness, consistency, and integrity in using Excel to perform analysis.

From Data Silos to Data Fabric with Knowledge Graphs


What Makes a Data Fabric? Data Fabric’ has reached where ‘Cloud Computing’ and ‘Grid Computing’ once trod. Data Fabric hit the Gartner top ten in 2019. This multiplicity of data leads to the growth silos, which in turns increases the cost of integration.

New Software Development Initiatives Lead To Second Stage Of Big Data

Smart Data Collective

The big data market is expected to be worth $189 billion by the end of this year. A number of factors are driving growth in big data. Demand for big data is part of the reason for the growth, but the fact that big data technology is evolving is another. New software is making big data more viable than ever. As new software development initiatives become more mainstream, big data will become more viable than ever. Structured. Semi-structured.

How to Avoid the 10 Big Data Analytics Blunders


Leading organizations are leveraging an analytics-driven approach—fueled and informed by data—to achieve marketplace advantages and create entirely new business models. Blunder #3: Not solving your real data science problem: dirty data.

Five Signs That You Might Have a Know-Your-Customer Problem


If your business is experiencing any of these symptoms, then you may be suffering from dirty, duplicate data. And these aren’t the only symptoms, making dirty, duplicate data very much a universal Silent Killer. In working with companies across many industries to help them to unify, clean and classify their customer data, I’ve learned two things: (1) there’s no one-size-fits-all solution and (2) staying on top of customer data demands constant vigilance.

Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data.

If Johnny Mnemonic Smuggled Linked Data


Let’s imagine Johnny Mnemonic – Keanu Reeves’ character from the cyberpunk action thriller movie of the same name – who is carrying a data package inside his head. He has been tasked with smuggling data – not in the 90s as the movie had it, but these days. Would Johnny be better off if the data to be transferred was Linked Data? A Headful of Linked Data. The deconstructed Johnny’s data problems are three: 1. Linked Data and Volume.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?


Whether you refer to the use of semantic technology as Linked Data technology or smart data management technology, these concepts boil down to connectivity. Connectivity in the sense of connecting data from different sources and assigning these data additional machine-readable meaning. Knowledge graphs are now also a buzzword among companies who are looking to integrate data from multiple sources and break the silos their legacy systems have left them with.

If Johnny Mnemonic Smuggled Linked Data


In this article, we are bringing science fiction to the semantic technology (and data management) talk to shed some light on three common data challenges: the storage, retrieval and security of information. We will talk through these from the perspective of Linked Data (and cyberpunk). Long story short, you will learn whether Johnny Mnemonic would have been better off, had he been tasked with smuggling Linked Data. If Johnny Mnemonic Smuggled Linked Data.

What is Data Variety?


Enterprise-level data is constantly growing and developing—and organizations are starting to recognize the value in collecting it. But when it comes to actually leveraging that data as an asset, enterprises are faced with several unique challenges. We often refer to the three Vs of Big Data –volume, velocity and variety. But many are facing a much bigger problem in the variety of their data than they are in the volume or velocity. Why is Data Variety Different?

Data, Databases and Deeds: A SPARQL Query to the Rescue


In this article you will read why and how SPARQL queries make for a better search and are of immense help when it comes to accessing all the independently designed and maintained datasets across an organization (and outside it) in an integrated way. Data, Databases and Deeds: A SPARQL Query to the Rescue. quintillion bytes of data created each day, the bar for enterprise knowledge and information systems, and especially for their search functions and capabilities, is raised high.

Competing in a Post-Analytics World


A big reason for the urgency is that a major barrier–clean, curated, classified and computable data at scale–is today being solved by human/machine collaboration. When you can accelerate the continuous availability, trustworthiness and reliability of the data that drives AI, you really open up the road for the autonomous enterprise. Today, AI-cleaned and -integrated data enables industrial-strength predictive analytics. Data-driven to the Max.

The Missing Link in Enterprise Data Governance: Metadata


We’re dealing with data day in and day out, but if isn’t accurate then it’s all for nothing!” In order to figure out why the numbers in the two reports didn’t match, Steve needed to understand everything about the data that made up those reports – when the report was created, who created it, any changes made to it, which system it was created in, etc. Steve needed a robust and automated metadata management solution as part of his organization’s data governance strategy.

AML: Past, Present and Future – Part III


The system must: Ingest, process, analyze, store, and serve all types of AML data, be it structured (database tables), unstructured (contracts, e-mails, etc.), Handle increases in data volume gracefully. Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews.

Data, Databases and Deeds: A SPARQL Query to the Rescue


quintillion bytes of data created each day, the bar for enterprise knowledge and information systems, and especially for their search functions and capabilities, is raised high. New ways of using legacy databases are put to work to bring integrated information at the fingertips of knowledge workers and to empower deeper discovery of data patterns and richer analysis of information grids. Normalizing data values (if needed). In a world of 2.5

Test principles – Data Warehouse vs Data Lake vs Data Vault

Perficient Data & Analytics

Understand Data Warehouse, Data Lake and Data Vault and their specific test principles. This blog tries to throw light on the terminologies data warehouse, data lake and data vault. It will give insight on their advantages, differences and upon the testing principles involved in each of these data modeling methodologies. Let us begin with data warehouse. What is Data Warehouse? The Reporting layer helps users retrieve data.

The Data Journey: From Raw Data to Insights


We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.

Know-Your-Customer and the Power of Now


If you don’t have clean data feeding your KYC programs, you can’t possibly have a real-world picture of your customers, one that’s trustworthy enough for making critical decisions. Customer-related data, perhaps even more so than other data, faces an uphill battle in the “clean data wars.” There’s the natural “drift” and disconnect that happens when so many different people are creating, adding to, and updating customer data as part of their daily jobs.