A Buyer’s Guide to 100% Success

written by Elizabeth Melton, MS, BA

Data integration is for companies that want access to all their data, in one place, without complex engineering. It can help

your business gain new insight, enhance automation, and make life easier. This guide is for businesses that want a quick and easy way

to find out more about data integration.

Diyotta Inc3700 Arco Corporate Drive, Suite 410

Charlotte, NC 282731-888-365-4230

www.diyotta.com

Why Data Integration Matters

Do You Really Need a Data Integration Solution?

Look Before You Leap!

Decisions, Decisions

Business Considerations

Buying 101

From Demo to Purchase

Competitive Landscape

Is Diyotta the Right Data Integration Solution for You?

About Diyotta

What We’ll Explore

01

02

03

04

05

06

07

08

09

10

Why Data Integration Matters 01

Data is produced with every click, share, search, or swipe. Any enterprise needs to analyze this data in order to make fact-based decisions. This has caused tremendous demand in the big data analysis market globally.

According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace big data will lose their competitive position and face extinction.

The amount of data available and the sources it’s coming from can be overwhelming to deal with. This necessitates the need for a data warehouse or Data Lake and thus an ETL/ELT tool.

But how to procure the ETL which best suits the requirement? It still remains challenging and tedious as most of us are familiar with ETL tools but don’t necessarily know how to select the right one.

Failing to choose the right one is exceptionally costly. In fact, Boston Consulting Group estimates that 60 - 75% of development costs come in the ETL layer.

To guarantee success, one must understand and evaluate the various factors in play and how they can help business stakeholders achieve their goals.

This guide will give you confidence and highlight the key points to consider while evaluating the tools to purchase. Additionally, you’ll be able to justify the return on investment by establishing reasonable success criteria in advance. Read on to prepare for your next promotion!

Do you Really Need a Data Inegration Solution?02

The explosion of data, combined with the global pandemic, has spurred a rise in hybrid cloud adoption. 86% of businesses consider a hybrid cloud environment their ideal operating model. Mixing private and public clouds with a traditional data center help satisfy dynamic business requirements, serve customers faster, and enable better decision making.

A hybrid environment with on-premise and cloud storage can better segregate your data and generate insights in real-time, all while saving you money. Over 40% of enterprises have planned or are planning to build a hybrid cloud infrastructure.

40% 42% 42% 52%Decline in

Ownership’sCost

EnhancedOperationalOfficiencies

FacilitateInnovation

EnhancedCustomer

Servcie

* Users polled had the option of choosing more than one primary reason as a response, leading to percentages totaling greater than 100.

Predictably, Bain found that 2 out of 3 CIOs plan to use hybrid cloud infrastructure providers to control costs. Their decision is valid.

A Microsoft-Zinnov study found that hybrid cloud deployment could result in 5-30% cost savings. The key to achieving such results is an excellent data migration accompanied by an advanced ETL tool as ETL solutions give context to tremendous volumes of data that, when treated separately, would have little meaning.

ETL solutions give context to tremendous volumes of data that, when treated separately, would have little meaning.

1. Aggregates raw data across all platforms and transforms them for reporting needs.

2. Automates much of the data handling process, thus

reducing human error and increasing accuracy.

3. Has built-in automation that eliminates many rote, unproductive activities like importing, sorting, codifying, etc.

4. Facilitates high-quality, data-backed decisions.

Look Before You Leap!

03

Did You Know? Data migration is the most important yet risky aspect of a data transformation project. According to Gartner, 83% of data migration projects fail or exceed budgets and schedules.

Data migration initiatives surpass projected costs by 30% on average and go over the allotted time frame 41% of the time. Keeping these pitfalls in mind is crucial when deciding whether to build or buy an ETL tool. No matter which way you go, everyone must be confident that the data coming from pipelines are fully tested and accurate, Remember, business stakeholders make enterprise-level decisions based on pipeline outputs, so avoiding data quality issues is paramount.

The best ETL vendors also leverage data processing platform capabilities you might already have, such as Hadoop, Snowflake, Google Big Query, and Amazon Redshift. They can support hybrid environments as well, using agent-based processing to source and move data between cloud and on-premise databases seamlessly. Plus, ETL vendors are continually working on new features. If you buy an ETL tool, your team will have access to the most up-to-date, scalable technology with every release.

Build ETL Solution Buy ETL Solution

Pros

Cons

You have complete control over your pipeline and the data fed into it.

Hand-coded systems not limited by rigid features & can be optimized to fit your company’s unique metadata.

You must source a team of engineers with the right skillsets to build & maintain the tool.

Self-built ETL tool demands constantmaintenence, documentation & testing by dedicated engineers.

Modern ETL tools’ automated data pipelines shorten development cycle & reduce potential error up to 80%.

“Zero code” ETL tools have drag & drop workspaces, enabling teams to create new data pipelines in a matter of clicks.

Pre-configured tools can be restricted by their out-of-the-box features..

Pre-built tools requireregular updates.

Decisions, Decisions

04

Whether you build or buy, you’ll have to pick an approach to data centralization: ETL or ELT. ETL and ELT processes perform the same set of operations - Extract, Load, and Transform but in a different order.

The approach you select will depend on whether you want to transform the data before or after loading it into the data warehouse.

ETL, or, Extract, Transform, Load, is the traditional technique used by on

premise solutions. This process involves extracting data from

each source, transforming it, and loading cleaned data

into a target database. The transformation stage

of ETL takes a significant amount of time. Modern ETL

tools have become much faster.

ELT on the other hand, is a faster process made possible by cloud data warehouses. Although the steps are the same, the order of operations differs. The transformation piece comes last since the cloud can parallel process numerous jobs at once. As a result, developers can spin up and tear down jobs quickly and make data views available to business stakeholders in real-time.

The approach you select will depend on whether you want to transform the data before or after loading it into the data warehouse.

FOR PREFERRED APPROACH

REASON

speedier data analysis

complianceadherence

immediate or real-timedata load

cloud-baseddata warehouse

flexibility

skillset

Data is already transformed, supporting fast and stable data analysis.

ETL is a more secure way of transforming data before putting it in the data warehouse.

ELT is used to speed up large, structured, or unstructured data loads. The transformation takes place post-load.

Platforms like Google BigQuery and Amazon Redshift have incredible processing and storage capabilities that make ELT pipelines possible.

ELT provides flexibility--you can transform certain data for specific analysis requirements.

ETL has been in use for a long time, so it’s easier to find data engineers with the night skills and experience.

Business Considerations 05

You might default to your budget when shopping around, but you shouldn’t buy based on price alone. Your ETL tool must fit your current business needs and be flexible enough to accommodate changes your business might make in the future. As such, build the following technical factors into your decision.

• Your ETL tool should be able to connect to all of your data sources, such as CRMs, ERPs, spreadsheets, legacy systems such as mainframe and Unix based applications, and other databases.

• Your ETL tool should be capable of handling multiple data and file formats and structured and unstructured data.

• Your ETL tool should be able to handle big data, and real-time streaming data from social media and networking sites is an added advantage.

Some ETL tools like Diyotta support more than 150+ built-in connectors for both source and target.

“Automated Insights” and “Augmented Analytics” are among the top ten technology trends per Gartner.

• Organizations need smart ETL tools with automated data pipelines to move huge volumes of data from source to target. • Organizations should adopt DataOps best practices. DataOps is an agile methodology designed to prepare data reporting automatically. Automated data pipelines reduce manual effort and repetitive work.

• ETL with smart DataOps is pivotal for business growth and agility. Smart DataOps processes can apply to data sources in hybrid environments, in-cloud, and on-premise.

• Enterprises should look for cloud-native data integration capabilities. This will help overcome data management challenges across multiple clouds, like Amazon Web Services, Google, Microsoft Azure, Cloudera, Snowflake, Salesforce.

• Intelligent, modern, cloud-native data management supports data quality, master data management, and data cataloging.

Built-In Connectors

DataOps & Automation

Cloud-native Data Integration

• Just like any system, your ETL tool should give continuous performance a granular level to enable your engineers to intervene and act rapidly.

• Alerts and email notifications should appear when jobs are running slowly, when jobs have failed, or if other aspects of the platform aren’t working. This will allow engineers to issue fixes before end-users bring problems to your attention.

Employees with a wide range of technical expertise might be using your ETL tool. Thus, your ETL tool should:

• Allow non-technical end-users to query data without understanding how the backend works. By the same token, more technical users should be able to write code, schedule jobs, and manage metadata. • Facilitate simple mapping between source and target data to ensure that the right data ends up in the right place.

Some ETL tools require developers to define mapping rules through code, whereas others provide drag and drop interfaces for non-technical personnel to generate their own mapping rules. Check for enterprise features like user roles and permissions, accelerators or wizards, command-line interfaces, and graphical user interfaces (GUI).

All businesses focus on security, but some have more stringent requirements than others. For example:

• An ETL tool for a healthcare company must be HIPAA Compliant.. • An ETL tool for a business operating in Europe must be GDPR compliant.

Regardless of industry, the tool should be SOC 2 compliant with end-to-end encryption.

Smart Monitoring

Ease of Use

Security

The volume of data a company collects will only expand over time.

• Modern companies need cloud-based ETLs to keep up.

• Cloud-based ETL tools have native processing, performance optimization, and bulk ingestion capabilities that can consume and process large quantities of data at high speeds.

Product support and pricing are essential factors to consider during your evaluation. Quality of technical support, documentation, and timeliness and completeness of vendor response are paramount to a successful implementation. Remember to pay attention to cost management, pricing, and contract flexibility as well.

• Your ETL vendor should offer different pricing plans for different needs and features.

• Your ETL vendor’s customer support team should supply step-by-step guidance during setup and beyond. This documentation is useful no matter whether you do an on-premise, cloud, or SaaS implementation. • Besides documentation, make sure you can contact someone 24/7. When something goes wrong, the vendor’s support team should be eager and able to help.

• A vendor with an excellent product, a customer-centric approach, and the lowest average costs is likely the best fit for your organization’s data integration strategy.

Data should be reliable, usable, and consistent.

• As a best practice, data should be cleaned and corrected at the source data level rather than in the data warehouse.

• An ETL tool with advanced data cleansing features lowers the potential syncing and replication issues.

Scalability

Product Support & Pricing

Data Cleansing

The processing power you need depends on whether or not your company needs access to data in real-time.

• Suppose your company doesn’t need to analyze data as soon as it comes in. In that case, batch processing is a cost-effective solution, allowing transactions to build up over time before processing and delivering in the target database.

• Of course, this isn’t good enough for some companies that need real-time reporting. If this is the case, seek out ETL tools with real-time processing instead.

• A single unified software that allows movement in batch and real-time is the best solution for any modern data ecosystem.

The above is not an exhaustive list, but it does come in handy during the evaluation process. A few other things to keep in mind are:

• Ease in performing data manipulation • Advanced configuration management and version control mechanism• Embedded business rules • Metadata management

Each of these other points depends on your company’s size, work locations, industry needs, and other circumstances.

Real-time vs. Batch Processing

Buying 10106

Before you start scheduling demos, you need to sort out which features are must-haves and which are merely nice-to-haves. This can be overwhelming, so begin by asking yourself these questions:

Utilize your answers to create a set of parameters and success criteria. With every demo, cross reference your list to see which tools check

the critical boxes. As you narrow down vendors, share your success criteria and ask for a proof of concept or a trial period so you can evaluate

each tool against those objectives. Not every vendor will take you up on your request but experiencing a tool first-hand can be

invaluable in your decision.

What is your budget?

What is your timeframe? Is this a rush implementation, or can you take your time?

What are the top business reporting/analytics needs?

Is your business proliferating?

What skills is your data team lacking? Will you need extra customer support?

Do you have a team that can perform required maintenance, or does your tool need to have

automatic updates out-of-the-box?

From Demo to Purchase07

Business Alignment

-Alignment of business

vison & goals

Business Requirements

-Capture strategic business

requirements & define

suture state of new

software

Software Requirements

-Identifyrequirments &score based on strategic importance

-Complete demos

-Scoring & Selection

Purchase

-Verify references

-Develop SOW

-Purchase negotiations

Implementation

-Kick off call

-Implementation

-Data Migration

-User acceptance

testing

-Production launch

- Organizational change

management

Competitive Landscape08

Is Diyotta the Right Data Intergation Solution for You?09

Picking the right ETL tool for your business pays off. Organizations that invest in big data notice a profit increase of 8 percent and a 10 percent reduction in overall cost. Diyotta has the best of all worlds. It can do ELT or ETL, it has a best-in-class scheduler, and it has the flexibility and scalability that enterprises demand.

It’s no surprise that Diyotta has happy clients across all industries. Health Lumen,a healthcare analytics company, saw over $100,000 in cost savings and a 50x improvement in data processing speed after implementing Diyotta. TechStyle Fashion Group, a women’s and children’s retailer, used Diyotta to capture massive volumes of data and use it to develop effective media campaigns and optimize their marketing budgets in real-time. With Diyotta’s built-in features, Scotiabank experienced 11x faster data movement, 7x faster scheduling, and 6x faster data extraction.

Diyotta puts a heavy emphasis on making ETL user-friendly for anyone, and it shows. Clients ranked Diyotta service and support as the best part of the implementation. With its broad canvas area and drag and drop functionality, Diyotta is easy for any end-user to learn. And with Diyotta’s newest upgrade, tech-savvy developers and business analysts can all get what they need out of a cost-effective tool.

About Diyotta10

Diyotta provides next-generation data integration tools. Our mission is to simplify and steam data ingestion and transformation, so data analytics teams can do what they do best. Discover new ideas, drive innovation, and inspire change. We provide an engine-free data integration platform that makes it easy to integrate huge volumes of data from any source to any target, whether on-premises, in the Cloud, or a hybrid environment.

Our senior executive team has more than 100 years of combined data integration experience. Enterprises like Sprint, Scotiabank, Clearsense, and Canadian Tire trust Diyotta technology for their data platform & operations. Diyotta is a technology partner of Amazon, Google, Microsoft, IBM, Snowflake, Cloudera, ThoughtSpot, and Splice Machine. To learn more about Diyotta and its solutions, visit: https://www.diyotta.com/

Want to Discuss in Person?

Diyotta Inccontact@diyotta.com

1-888-365-4230www.diyotta.com