As a business leader in the acceleration economy, your success relies on accurate data, real-time analytics, and actionable intelligence. This a simple statement to make, yet it’s daunting to build the data infrastructure, hire and develop the talent, and create a data-driven culture.
Exacerbating the challenge, business leaders are frequently presented with a new data tool or AI platform that promises to “change everything.” That’s why it’s important to regularly sit down with your IT and data leaders to assess your current data and analytics technology. This discipline identifies gaps and requirements and empowers you to prioritize data initiatives and funding.
Why You Need a Data Technology Essentials List
We’ve put together an executive cheat sheet to help you identify data technology and tools to accelerate your journey from data to insights. For those leaders just getting started, or those who feel as though their data strategy and tech are stalling, this guide will help provide a foundation for the effort. More advanced leaders can look at the core tech outlined below as a way to “clean out the data tech closet” and focus on the essentials required to uplevel their use of data.
The data tech and tool list outlined below is not exhaustive — that list requires a book — and the number and types of tools can vary greatly depending on your sophistication and use of data. We’ve broken it down by requirements and general solutions across three critical technology functions.
Which companies are the most important vendors in data? Check out the Acceleration Economy Data Modernization Top 10 Shortlist.
Where to Keep Your Data
Requirements: My organization needs to store, manage, and process data aggregated from multiple sources inside and outside the organization.
Technology solutions: Here are four platforms that can be used together or as standalone tools.
- A database is foundational to your requirements. You may have multiple databases housing different types of data for different types of applications such as financial or sales. Databases perform best when there’s a single source of structured data and have limitations at scale.
- A data warehouse is an information system that analyzes, reports, and integrates transaction data from different sources, including databases. A data warehouse can serve as a single source of truth for the organization for the decision-making and forecasting process. It only works with structured data.
- A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits.
- A lakehouse combines the best elements of data lakes and data warehouses using an open architecture. For those who are starting from scratch, this is quickly becoming an intriguing platform. The advantage is cost, as lakehouses run on low-cost cloud storage in open formats.
How to Ingest and Analyze Data
Requirement: My team needs to use and analyze data from multiple sources across multiple databases. We want the machines to do as much heavy lifting as possible. We want our talent to interpret data and turn it into intelligence.
Technology solutions: These are five of many platforms and tools that will enable your business to capture data and capitalize on it for business insights.
- ETL (extract, transform, load) tools: ELT is a method of moving data from various sources into a data warehouse for analysis. It is essential to be able to move different types of data easily, deduplicate the data, and conduct data cleanup for proper use. This tool set should include data quality and validation capabilities to ensure data accuracy and completeness.
- Data lineage tools: These tools follow and chart the lifecycle of data creating a visual representation of the overall flow of data. They visualize how data is manipulated to assess the quality of data before it is loaded into BI or analytics tools.
- Robotic process automation (RPA) with artificial intelligence (AI) tools: RPA tools can significantly increase the speed and reduce the manpower required to move, crunch, and manage data. These tools and bots assess and automate mundane tasks and streamline workflow processes. And the tools represent a smart capability — low code/no code software — that should be included in your data tech to increase both speed and usability.
- Business intelligence (BI): BI tools collect, process, and analyze large amounts of structured and unstructured data from both internal and external systems. Data sources can include documents, images, emails, videos, journals, books, files, and more. BI tools find info through queries and present data in user-friendly reports and dashboards.
- Master data management (MDM): MDM tools and processes are used to create a single master record for each person, place, or thing in a business from across internal and external data sources and applications. The data is de-duplicated, enriched, and verified to improve data accuracy.
Security, Storage, and Backup to Protect Data
Requirement: I need to ensure we have stringent data privacy, security, and access policies and processes to protect customer privacy and company intellectual property (IP), while complying with specific regulations.
Technology solutions: These technologies likely are already part of your company’s cybersecurity, IT, and cloud investment. It’s smart to understand additional requirements for advancing your use of data.
- Data replication: These tools provide frequent electronic copying of data from a database in one computer or server to a database in another. Data users can quickly access data relevant to their tasks without interfering with the work of others. This not only delivers speed, but can also play a role in disaster recovery.
- Data security: Data security requires an integrated set of tools to protect data from unauthorized access and includes network, physical, and file security through its entire lifecycle. This long list of technologies — including firewalls, access control, data encryption, and data loss prevention — should be integral to your company’s cybersecurity strategy and infrastructure.
- Data governance: Data governance software helps create, manage, and enforce your governance program. It manages everything from data availability to data integrity in the context of data governance.
- Data storage and backup: Data storage systems hold data files in a secure location, from which they can be accessed easily. Data backup, in contrast, refers to saving additional copies of your data in separate physical or virtual locations from the original data files in storage. These two processes work hand in hand.
In the acceleration economy, customers change in seconds, markets shift in minutes, and business threats and opportunities crop up daily. As you can see from the cheat sheet above, it takes serious tech and strategy to turn bits, data files, and records into intelligence and business advantage. That’s why it’s so important to continuously lock arms with your IT and data colleagues to advance the use of data and analytics in your business.
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