Entity Matching in Inzata

Entities Matching is a crucial component in the process of data integration, and it is supported by Inzata’s advanced AI/ML library. The library is designed to handle deduplication and record linkage at scale, using an unsupervised learning approach. This approach allows for the identification of similar records, even when there is no explicit identifier that links them together.

One of the common scenarios where entity matching is required is when multiple distinct records relate to the same entity, but there is no way to connect them. For example, customers may have made orders under different accounts or without registration, and the data may come from multiple sources. This can lead to a large number of duplicate records, which can negatively impact the quality of the data and the accuracy of any analysis or reporting that is based on it.

Typographical errors are another major issue that can affect data quality, especially when it comes to identifying entities. These errors can occur when data is entered manually, and they can make it difficult to match records that should be linked together. Additionally, changes in entities’ identification information can also make it challenging to match records.

Inzata’s AI/ML library addresses these challenges by using advanced algorithms to detect and integrate the same entities before further data processing. This allows organizations to improve the accuracy and completeness of their data, resulting in more accurate and reliable insights and decision-making.


What is NoSQL? Non-Relational Databases Explained

Non-tabular NoSQL databases are built on flexible schemas, with nested data structures, to accommodate modern applications and high user loads. Increasingly, they’re the optimal choice when ease of development, scaled performance, speed, and functionality are central operating concerns.

What is NoSQL?

NoSQL stands for “not only SQL” to signify that it accommodates various data access and management models. “Nonrelational database” is also interchangeable with “NoSQL database.” Programmers developed the language in the mid-2000s alongside the widespread adoption of new information trends. 

Some people insist that NoSQL databases are sub-par when it comes to storing relational data. However, this is an unfair argument. Nonrelational databases can handle relationship data; it’s just done differently. Plus, other folks may argue that relationship modeling is more manageable in the NoSQL environment because data isn’t parsed between multiple tables.

What? Can You Please Explain in Non-Tech Talk?

Does this all sound like Greek to you? If so, think of SQL and NoSQL as vinyl records and digital music streaming, respectively. To play a song on a vinyl album, you must physically move the needle on the turntable to access the “track” you want because the record was pressed ahead of time, and that’s where the desired “files” reside. Thanks to remote access memory, however, when streaming digital music, you can just press or click to play a song — even though all the parts making up the file may be scattered.

It’s not a perfect analogy, but the technological gist falls into the same category.

A Brief History of NoSQL

Back in the second half of the 20th century, when digital computing was still in its infancy, relatively speaking, data storage was one of the most expensive aspects of maintaining data sets. But by the late 2000s, on the back of Moore’s Law, those costs had plummeted, and developers’ fees were instead topping cost lists.

Back then, social media and digital networking were skyrocketing in popularity, companies were inundated with mounds of raw data, and programmers were adopting the Agile Manifesto, which stressed the importance of responding to change instead of remaining slaves to procedural protocol. As such, new tools were developed to accommodate the influx of information and the need to be highly flexible.

The practical and philosophical shift served as an inflection point and changed the course of computing history. Developer optimization was prioritized, flexibility took priority, and the confluence of events led to the creation of NoSQL databases.


Before NoSQL, there was SQL — the querying language used for relational database environments where tables are constrained and connected by foreign and primary keys.

On the other hand, NoSQL allows for greater flexibility and can operate outside the confines of a relational database on newer data models. Plus, since NoSQL-friendly databases are highly partitionable, they’re much easier to scale than SQL ones. 

To be clear, SQL is still used today. In fact, according to reports, as of May 2021, approximately 73 percent of databases run on the SQL model — but NoSQL is rapidly expanding its market share.

Why Do Developers Use NoSQL?

Developers typically use NoSQL for applications that process large amounts of data and require a low latency speed. It’s achieved by easing up on consistency restrictions and allowing for different data types to commingle.

Types of NoSQL Databases

There are five main types of NoSQL databases.

  • Key-Value: Key-value databases can be broken up into many sections and are great for horizontal scaling.
  • Document: Document databases are great when working with objects and JSON-like documents. They’re frequently used for catalogs, user profiles, and content management systems.
  • Graph: Graph databases are best for highly connected networks, including fraud detection services, social media platforms, recommendation engines, and knowledge graphs.
  • In-Memory: In-memory databases are ideal for apps requiring real-time analytics and microsecond responses. Examples include ad-tech and gaming apps that feature leaderboards and session stores.
  • Search: Output logs are an important part of many apps, systems, and programs. Search-based NoSQL databases streamline the process.

In a nutshell, NoSQL is a database revolution that’s helping drive tech innovation.


7 Ways to Optimize Your SQL Queries for Production Databases

According to Google, 53 percent of mobile users will abandon a website if it doesn’t load within three seconds — and the bounce rates for PCs and tablets aren’t that much more. So what do these stats mean for coders? Ultimately, they’re a stark reminder that crafting optimized SQL queries for production database environments should be a priority.

What Are SQL Queries and Production Databases?

New programmers may be wondering: What are SQL queries and production databases? At first, the terms may sound intimidating. But the reality is simple: SQL queries are simply the code you write to extract desired records from a database, and a production database just means “live data.”

In other words, a dynamic website that’s live and accessible is likely working off a production database.

Why Should SQL Queries Be Optimized?

Which would you rather read: a loquacious tome freighted with filler words and pretentious tangents or a tl;dr summary that zeros in on the topic at hand? Moreover, which would take longer to digest? 

The same keep-it-simple logic applies to writing in SQL: the best queries are short, sweet, and get the job done as quickly as possible — because cumbersome ones drain resources and slow loading times. Plus, in the worst-case scenarios, sloppy queries can result in error messages, which are UX kryptonite.

Seven Ways to Optimize SQL Queries for Production Databases

#1: Ask the Right Questions Ahead of Time

Journalists have long understood the importance of who, what, when, where, and how. Effective coders also use the five questions as a framework. After all, every business has a purpose, and, like a finely tuned car, every mechanism should support the company’s ultimate goal — even the SQL queries that power its websites, databases, and reporting systems.

#2: Only Request Needed Data

One significant difference between novice programmers and experienced ones is that individuals in the latter category write elegant queries that only return the exact data needed. They use WHERE instead of HAVING to define filters and avoid deploying SELECT DISTINCT commands unless absolutely necessary.

#3: Limit Sources and Use the Smallest Data Types

If you don’t need a full report of matching records, or you know the approximate number of records that a query should return, use a LIMIT statement. Also, make sure to use the smallest data types; it speeds things up.

#4: Be Minimalist, Mind Indexes, and Schedule Wisely

Choose the simplest and most elegant ways to call up needed data. To state it differently, don’t over-engineer. Moreover, make use of table indexes. Doing so speeds up the query process. Plus, if your network includes update queries or calls that must be run daily, schedule them for off-hours!

#5: Consider Table Sizes

Joining tables is an SQL query staple. When doing it, make sure to note the size of each table and always link in ascending order. For example, if one table has 10 records and the other has 100, put the former first. Doing so will return the desired results and cut down on query processing time.

#6: Only Use Wildcards at the End

Wildcards can be a godsend, but they can also make SQL queries unruly. By placing them at the beginning and end of a variable, you’re inefficiently forcing the broadest possible search. Instead, get specific. And if you must use a wildcard, make sure it’s at the end of a statement.

#7: Test to Polish

Before you put a project to rest, test! Try different combinations; whittle away at your queries until they’re elegant code blocks that make the least number of database calls. Think of testing as the editing stage, and revise until the work is polished.

Who Should Tweak SQL Queries?

People with little or no coding experience may be able to DIY a small CSS change or add an XHTML element without catastrophe. But SQL queries are a very different story, one errant move can wreak mayhem across your operations.

Optimizing SQL queries is essential in today’s digital landscape, and failing to do so can lead to decreased views and profits. So make sure to optimize the code before going live. And if you don’t have the experience, enlist the help of someone who does. It’s worth the investment.


Top 3 Frustrations in Preparing Data for Analysis

The era of Big Data is upon us, and with it, business leaders are finding new insights in their data analytics to drive their tactical and strategic decisions. Data visualization tools are widely available from many vendors including Tableau, Qlik (Qlikview) Microsoft (PowerBI).

The question is no longer ‘are you using Big Data?’ but rather, ‘why not?’

Visualization vendors make data analytics sound easy; make your data accessible to our tools, push a button, and wondrous visual displays uncover never before seen insights into your business. One fact, however, is always downplayed; you actually have to prepare data for analysis. Great visualization tools are worthless if the data is not organized and prepped beforehand. For many data analysis projects and initiatives, the data prep itself can be the most time-consuming and repetitive step.
Here are, from our point of view, the top 3 challenges of the data prep process, and how to overcome them.

Frustration #1: Merging Data from Different Sources

Analysts want to jump right into the data analytics and uncover the promised insights, but first they have to follow the processes for data loading and making the data available to the analytics engine. Easily done if all of the necessary data is in a single data set; but it rarely is.
Data exists in many different systems, from finance to engineering to CRM systems, and both public and private sources. The number one challenge for data prep is the data munging and merging that must take place as you merge data from different systems. And it’s never easy. Simple nuances in the data are often the toughest part. 
The data, data structures, and even the definition of what the data reflects varies from one system to another, and the primary challenge of the data transformation is to merge it together in a consistent manner.
Time stamps that contain both time and date in one file, but time and date are in separate columns in another file, and must be merged first. Something as simple as how phone numbers and zip codes are formatted can wreak havoc on your results. This is the unspoken reality for the data analyst or scientist: the data is often not your friend.
At Inzata, we watched customers struggling with this challenge, and we have a better way. We noticed that a lot of the work was repetitive, and often involved simple (for a human) operations on the data. So we developed Artificial Intelligence that could perform these tedious tasks, requiring only occasional guidance from a human. We call it AI-Assisted Data Modeling, and it takes you from raw, disorganized data to beautiful analytics and visualizations, in under 30 minutesData analytics is no longer a strenuous task with Inzata’s full service platform. 

Frustration #2: Lack of Common Ground between the Analyst and IT

The analyst is a subject matter expert in her field, the IT pro knows the systems. But quite often, they don’t know much about the other’s role, and can’t speak the same language on requirements to prepare data for analysis. The analyst requests data from the IT pro, and files get sent and delivered in email and dropboxes.
In many cases, the data munging process becomes one of trial and error (request a file, work on it, discover changes, request a new file) until finally, after many iterations, the output of Microsoft PowerBI, Qlikview, Tableau or whatever other analytics tools are used delivers the right content.
But what if you could work with data in its native source format, coming directly from your source systems, with no ETL middleware or IT guy to call? Inzata lets you organize your connections to source systems (both inside your company and in the cloud) and reads in data in its native physical form. This makes the first steps of data analysis a breeze.
Inzata then helps you rapidly create business data models mapped to this native structure. Your days of transforming raw data and generating new files for analysis are behind you. We’ve taken these tedious tasks of data analytics and did it for you.
Everything else you do in Inzata is driven by these virtual data models, so your users and analysts only see highly organized data, structured and merged in a way they can understand it, because it resembles your actual business. Updates are no problem, when new data is ready from source systems, it automatically updates the Inzata dataset and your reports update in real-time.
Field names are in English, not computer-ese, and oriented around the things you care about. Customers. Transactions. Employees. These are the “things” you interact with in Inzata, just like in the real world. Data is displayed visually, no code to write. Data aggregations, rollups and filters become their own reusable objects. Creating a new dashboard is as simple as dragging those elements to a blank canvas and giving it a name.
What if something changes in the source system; Field names change or new fields are added? In the past this would wreck everything. Reports would stop working and you’d have to start over from scratch. Inzata anticipates these slowly-changing-dimensions, and detects when they happen. It automatically reallocates data elements to accommodate changes, and everything keeps working. The end result: you don’t need to worry about changes in source systems wrecking your reports and dashboards.
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Frustration #3: Missing Audit Trail

This part is very important for anyone who uses (or is thinking about using) a Data Prep tool, Excel or something similar that outputs files.
Insights gained through data analytics can give decision makers reason to make significant changes to the business. A lot is riding on these decisions, and there has to be confidence and accuracy in the data and insights. But after the data is merged from several sources, goes through various transformations, and gets reloaded, it becomes hard to track backwards from the insight to the original data. Honestly, are you going to remember the exact transform steps you did on a dataset you haven’t touched in 3 months? The lack of an audit trail weakens the confidence that the team can have in the outputs.
As a data scientist, you’ve worked hard to develop your reputation and credibility. Shouldn’t your tools also be up to the challenge?
By their very nature, file-based Data Prep tools cannot deliver this kind of confidence and auditing, because they are only in possession of the data for a short time. There’s nothing to link the final file with the original data, or the process of data analysis it underwent in the tool. They don’t maintain chain-of-custody to protect the data.
Inzata does.
From the moment your data enters Inzata’s ecosystem, every activity is meta-tagged. We track everything that happens with your data, who touches it, accesses it, what transformations or enrichments it goes through. We also have intelligent temporal shifting, which is a fancy way of saying we invented time travel. (At least for your data, that is.)
Here’s how: Inzata stores each incremental state of your data. If you want to see exactly how a report looked 3 months ago, we can show it to you. If you need to re-create a list-query exactly as it would have looked 5 days ago, we can do that.
Data preparation, and the challenges entailed, is the dirty little secret of big data analytics. The potential insights into your business are valuable, but the process can be so frustrating at times that the projects die on the vine. It’s time to spend as much time looking at data transformation tools that can take the human out of the equation as you do looking at data analytics tools.

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Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter