Can you imagine what it was like working as a data analyst in the early 2000s? We can't either, but it must have been so much easier, in some ways. They probably relied on traditional tools like Excel and gathered information from fewer sources, though they also probably had to do a lot of tedious manual work that we can automate today.
Now, we’re on a data-driven rollercoaster. You have to keep up with information from several databases, websites, social media sites, and more. At the same time, the data analysis field is ever-changing. If you can't keep up, you’ll find yourself behind the curve.
This article will increase your competence in data preparation and help business users thrive in this dynamic environment. You'll learn about self-service data prep tools, including selling points, key features, and even limitations.
No matter what sector you are in, you’ll get all the information that you need to evaluate data preparation tools.
What are data preparation tools?
Most raw data is unusable because it is dirty, especially if pulled from multiple sources. To weed out irrelevant, inconsistent, and other bad data, you can use data preparation tools. The information can then be analyzed and used for business intelligence (BI).
For example, if you’re a data analyst who supports a marketing team, you can help the team meet their objectives. With well-prepared data, they can create ad campaigns that customers are looking for. After disseminating the messages, analysts can help examine how well the ads performed.
Over the years, companies like Coca-Cola have used data to create ad content, inform sales plans, and improve their products. While we may not know the Coca-Cola recipe, we know their secret to a better customer experience is making data-driven decisions.
Many other organizations are doing the same. According to an Interactive Advertising Bureau (IAB) survey, in 2023, 36% of advertising decision-makers considered using data to check how attentive audiences were to their messages. Such businesses are generally successful in keeping their customers longer. Use this information to your advantage.
If you’re ready to level up your analysis game, we’ve reviewed 10 data prep tools to prepare your data for analysis.
#1: Pecan AI
Why Pecan AI: Pecan can not only get your data ready for predictive modeling, but it can handle that for you as well.
Pecan AI features: This award-winning software has customizable SQL templates, automatically engineered features, and an easy-to-interpret dashboard.
What sets it apart:
- Quickly making predictions.
- Highly secure to ensure customers’ data privacy and confidentiality, with no need for PII to accomplish predictive modeling.
- Detecting patterns as well as checking for gaps, duplicated entries, and distinct values.
Areas of limitation:
- While it is commonly used by B2C organizations, B2B companies can also use it.
#2: Informatica
Why Informatica: Informatica is a versatile tool that deals with data using an intelligent data cloud.
Informatica features: This platform has key features like data integration, quality, and maintenance. Its integration capabilities connect the dots without needing different components.
What sets it apart:
- Meeting the demand for low-code solutions
- Using AI to minimize data preparation efforts
Areas of limitation:
- For newbies, using Informatica might be scary. A little training is necessary before they get to work.
#3: SAP Data Intelligence Cloud
Why SAP Data Intelligence Cloud: SAP Data Intelligence Cloud is software for business analysts who deal with endless rows and columns. Since it's a cloud platform, it's ideal for working from home, the office, or a coffee shop.
Data Intelligence Cloud features
SAP Data Intelligence Cloud comes with assets like data integration, data visualization, machine learning, and monitoring.
What sets it apart:
- Transforming data into visualizations
- Pulling data to get the full scoop on customer behavior
- Speed at categorizing data from several sources
Areas of limitation:
- While SAP Data Intelligence Cloud is great when it comes to SAP products, it doesn’t speak the language of non-SAP tools.
#4: Datameer
Why Datameer: If you’re working with messy data, Datameer is your go-to tool. It’ll collect, analyze, and visualize data. What's more, it's not scared of massive data due to its Hadoop powers.
Datameer Features
The platform’s bag of tricks includes data cleansing, blending, enrichment, advanced transformation, and organization.
What sets it apart:
- Handling expansive data sets
- Fast data importation through Java Database Connection (JDBC) and other protocols
- Used by people who are new to the data game due to its friendly interface
Areas of limitation:
- If you’re a multitasker, Datameer might not be for you. It's impossible to use two windows in the same session.
#5: Qlik
Why Qlik: Qlik has extensive capabilities for complex data modeling, calculation, and analysis.
Qlik's features
Some features of Qlik include smart visualization and analytics, associative models, mobility, alerting, machine learning, sharing, collaboration, and reporting.
What sets it apart:
- Easy-to-understand visuals
- Finding connections between data columns
- Keeping an eye on data and issuing alerts when something is off
- Manipulating large data sets quickly and accurately
Areas of limitation:
- Its less user-friendly interface can be challenging to work with.
#6: Trifacta
Why Trifacta: Trifacta creates and automates data pipelines. If you’re using a service, the analytics solution will connect to it to host data.
Trifacta features
Collaborative interface, connectivity, machine learning, and automation are some of the key features of Trifacta.
What sets it apart:
- Tidying up data before analysis
- Multitasking
- Handling repetitive tasks, saving time and energy
Areas of limitation:
- While Trifacta is a fantastic organizer, it can be unpredictable for server run times, so flexibility may be required to work on its schedule.
#7: Integrate.io
Why Integrate.io: Cloud-based Integrate.io allows organizations to integrate, process, and prepare data on the cloud.
Integrate.io features
Loaded with pre-built connections and data security and compliance measures. It also has a simple, interactive interface and 100 integrations to different sources.
What sets it apart:
- Planning for data pipelines when you need them
- Turning chaotic information into orderly data
- Gathering data from multiple sources and funneling it to your IT systems
Areas of limitation:
- If you use Excel or CSV files, you’ll need to find another tool. Integrate.io only works on cloud-based data sources.
#8: Tibco
Why Tibco: With no-code process automation, you can achieve more in less time when using Tibco.
Tibco features
Its data modeling, friendly user interface, and web-based connectivity make it simple to use this business intelligence solution.
What sets it apart:
- Bridging the frustrating data gap between different tools and systems
- Integrating into an organization's workflows – once it's up and running, it can function for extended periods without major issues
- Uniting scattered data islands into one central view
Areas of limitation:
- Tibco's got tons of great features, but if you're new to it, you can get lost. Integrating it with other tools can be tough, too.
#9: Microsoft Power BI
Why Microsoft Power BI: Microsoft Power BI has a different approach to organizing and centralizing data by using data flows.
Microsoft's Power BI features
One of its strongest points is its customizable dashboards and navigation pane. If you’re used to working on Excel, you will love Power BI’s Data Analysis Expressions (DAX) function.
What sets it apart:
- Connecting to live data
- Tailoring visualizations to your preferences
- Encouraging productivity with minimal training and experience
Areas of limitation:
- Microsoft Power BI loses its shine when dealing with a lot of data.
#10: Tableau Prep Builder
Why Tableau Prep: Tableau Prep is your go-to platform if you’re tired of dealing with unruly Excel spreadsheets. It can clean and structure data in real time.
Tableau Prep features: Tableau has an intuitive, visual interface. It is also good at data cleaning, field formatting, filtering, and pivoting.
What sets it apart:
- Promoting group activities within the business intelligence team
- Performing multiple data cleaning actions at once, speeding up the process
- Collecting and organizing data from all digital corners, including Google Drive, Dropbox, PDF, and Text Files
Areas of limitation:
- Tableau Prep’s Achilles heel is that it can be slow when working on massive data sets. Further, its predictive capabilities are extremely limited.
Conclusion
Businesses produce data by the truckload every day. We're talking sales figures, contact lists, customer demographics, the whole shebang. Don't let this data go to waste. Your organization can use this information to make business decisions. But first, you need to prepare and analyze it.
Business analysts can use preparation tools to turn trash into treasure. Clean data can help a company understand what makes its customers tick, providing invaluable information for decision-making and strategy across departments.
That's why we highlighted all the best features and limitations of these tools. To take the next step, explore what Pecan has to offer and watch your business thrive.
