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From Manual Processes to Automated Results

It takes more than tools to build a home

If someone told you that you and a team of your choosing had one week to produce a two-story home to sell at a premium, what would you do? What if you had to then scale that production internationally? Would you consult an architect or an electrician? Binge-watch construction videos? Buy a bunch of tools, such as a power drill and nail gun? Or would you hire a construction crew to do it for you?

73% of organizations indicate analytics spend will outpace other software investments.
4 Ways to Unlock Transformative Business Outcomes from Analytic Investments, IDC

Your decision would depend on the time, talent, materials, and equipment you had at hand and, most importantly, which worked best for scaling up.

While you most likely won’t be tasked with building two-story homes any time soon, you’re probably facing similar problems where you work. Instead of Tudors and Victorians, it’s data and growth. And even though the goals are different, you might want to go about it the same way you’d solve the construction dilemma.

When tasked with scaling your analytics solution, maybe you took some online data analytics courses or encouraged your team to take them. Maybe you went out and added data analytics tools, hired analysts and data scientists to work with your IT team, or brought in a third-party data company.

These solutions probably helped a little, at least in terms of getting to the data results more quickly, but they probably didn’t help you scale. You got all the tools and materials, but you didn’t get the house.

48% of companies are investing in training staff.
30% of companies are investing in creating new jobs.
Investments in Big Data

More hammers don’t equal more homes

Like power drills and nail guns, many of today’s data analytics tools deliver faster and more efficient access to data, data integration, ETL, data prep, BI, and visualization. There’s nothing wrong with that, per se. After all, a nail gun is better than a hammer for quickly nailing boards together, but a nail gun alone doesn’t lead to a house the same way a data warehouse alone doesn’t lead to transformative business outcomes.

Do you still want the nail gun for your home and the data warehouse for your business? Absolutely. But no data tool by itself empowers you to drive real business results through automated data extraction, preparation, enrichment, analytics, data science, and reporting.

62.4B hours spent on data and analytics are lost annually worldwide.
Data and Analytics in a Digital-First World, IDC

Think of your current tools as blueprints or instructions. They’re great for showing you how to get the results but not for addressing skills gaps. They don’t turn someone who’s never built anything before into a construction expert overnight.

Maybe that doesn’t seem like a problem right now. After all, maybe your business is profitable. Maybe you’re even a leader in your industry. No, you can’t make a two-story Tudor in a week, but no one else can either. Besides, sales are good. People are still buying. You still have time to figure out the next big thing.

But that’s where you might be mistaken

7 hours per week are wasted by data workers on repetitive work every time a data source is updated or refreshed.
Data and Analytics in a Digital-First World, IDC

Imagine trying to build a house based on descriptive data. Even if you had a team with all the skills needed to build a house, you’d still have to decide how to build the house and what type to create. You would either need to rely on data and market insight or intuition and your gut. You’d have to agree as a team on which one would produce the most profit. Would you go with a past model or try something different for upcoming trends?

The problem with most automation and analytic point solutions today is that they cover only one part of the process, are limited to a few users instead of the entire organization, or only help you prep and blend data and not predict.

This means you’re not only behind your competitors when it comes to data, but now you also have knowledge and skills gaps. You can’t close those gaps with a tool

But you can with a transformative platform.

Build Any Home With Analytics Automation

What you need to build a home in a week with any team is a platform that removes the guesswork. The platform would automate every home-building task, and anyone could use it, whether they were just starting out or had built homes before. It would handle highly-specialized skills such as electrical wiring, help you scale up on a global level, and adapt as new styles and models were needed. All you would need to do is supply the materials the platform needed and the outcomes you wanted.

That’s what you want for your business, too. When it comes to data, what you need to truly innovate and accelerate digital transformation is a data and analytics platform that engages your whole organization and is focused on people, processes, and data.

Enter Analytics Automation. This end-to-end software automates repetitive tasks and turns the creative aspect of data analytics over to you — and everyone on your team. Instead of spending your time downloading, importing, and configuring data, you spend your time searching for the answers you want based on the ideas you have.

Analytics Automation covers the entire analytic process: data access, cleansing, profiling, enrichment, preparation and blending, predictive analytics, machine learning, and automated business insights. It can automatically create dashboards and help you dig into the numbers using almost any data type or source. And you can do all of this without knowing any code or having a background in data science.

If you do know code, then you can still use that, too.

With Analytics Automation, you can build any data pipeline, analytic workflow, or model you want — just like you were building a home.

Three pillars of digital transformation
#1 Data
#2 Process
#3 People

Analytics Automation makes digital transformation possible by bringing together the three key aspects we talked about before — making data and analytics available to everyone, automating business processes, and allowing everyone to easily learn new data analytics skills that speed up business outcomes.

It’s that last one — helping people across the organization drive outcomes — that distinguishes Analytics Automation from other data tools. Anyone who uses it starts with an end in mind. Every business objective and outcome achieved with Analytics Automation — whether in marketing, product, sales, HR, or another department — brings together data assets, critical business processes, and people’s domain knowledge.

The result is faster analytic outcomes in the areas of top-line growth, bottom-line return, efficiency gains, and rapid education of your workforce.

The Alteryx Analytics Automation Platform

Unified, end-to-end analytics, data science, and process automation

Analytics Automation takes care of almost everything you need to drive better business decisions: automation of repetitive processes and tasks, data access, cleansing, profiling, enrichment, data preparation and blending, predictive analytics, and machine learning. All of it. And it’s all in a self-service data analytics platform that anyone can use, whether they’re a data scientist that wants to use Python or a human resources manager fresh out of college.

You decide what you want to find and set up the workflow; Alteryx does the rest. You can use Alteryx with every data type and source, from your legacy on-premises databases to modern cloud data warehouses and cloud applications. To help highlight the differences between Analytics Automation and all of the other tools available today, take a look at the following pages which break down some of today’s most popular implementations.

The Alteryx Analytics Automation Platform Versus Point Tools

Tool & Analytics Automation Comparison: RPA/BPA

Although different, Analytics Automation and RPA/BPA complement each other. RPA/BPA bots can send data to Analytics Automation platforms. Analytics Automation platforms then take that and then send data outcomes into enterprise applications.

Similarities to Analytics Automation

  • Automates repetitive and tedious business
  • Processes normally completed by people
  • Works in any industry
  • Reduces error
  • Performs simple tasks relatively easily

Limitations

  • A single change can cause automation processes to fail
  • Costs and complexity go up with the number of bots and processes added
  • Interactions between different technologies and platforms present problems
  • Susceptible to regulation changes
  • Requires knowledge to set up
  • Limited in capabilities, including lack of ML, AI, and predictive and prescriptive analytics
  • Automates low-level tasks but doesn’t provide automation of advanced analytics or create opportunities to upskill people

The Analytics Automation Advantage

Lifts up to 80% of the burden with end-to-end, complex process automation, complemented by RPA inputs and outputs.

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Tool & Analytics Automation Comparison: Data Integrator/ETL

These tools help move or copy data from their original source(s) and import them into analytics tools and platforms for merging with additional datasets and formats.

Similarities to Analytics Automation

  • Access and stores data
  • Visualizes data flows
  • Parses, cleans, enriches, and validates data
  • Exports to reports

Limitations

  • Needs months to implement
  • Takes time to process data; no real-time access to data
  • Requirement changes involve months of rework — hard to make changes; need to see server users as an example; likely only to pull specific information
  • Reports frequently need revisions/iterations to be suitable for business decisions
  • Requires specific knowledge to set up and use — Requires expertise and SQL; hard to truly understand what’s going in the process; hard to visualize it
  • Little to no self-service data analytics implementation and use
  • Limited in capabilities, including lack of ML, AI, and predictive and prescriptive analytics
  • Doesn’t provide the ability to perform or learn advanced analytics

The Analytics Automation Advantage

Incorporate millions of data points from multiple datasets to reduce processing time of reports from months to minutes.

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Tool & Analytics Automation Comparison: Data Prep

These tools are IT-centric or end-user tools focused on source-to-target mapping and transformation of data into data warehouses and data lakes that can take months to implement and often require knowledge of SQL.

Similarities to Analytics Automation

  • Basic and augmented/smart data preparation — parsing/cleaning/enrichment of data
  • Designed for data analysts to create a data pipeline

Limitations

  • No analytic capabilities or data enrichment
  • Sporadic cloud storage
  • Hard to combine data sources
  • Size of data; limit on the sizes you have (i.e., spreadsheets with a million-plus rows); charged for bigger datasets
  • Little to no self-service data analytics implementation and use
  • Adopts spreadsheet-like UI, which limits advanced analytics and machine learning pipelines, plus transparency and collaboration
  • Lack of self-service and drag-and-drop options and advanced analytics capabilities

The Analytics Advantage

Reduces prep-to-result time and automatically feeds it into predictive and prescriptive models. As new data sources are added, it scales with it, automatically cleaning, parsing, and combining multiple data sources based on the previous settings.

50M Data Rows In Minutes
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Tool & Analytics Automation Comparison: Advanced Analytics, Data Science, Machine Learning, Automation

Typically standalone options are available and accessible only by data scientists, limiting the upskilling of a workforce and creating data analytics queues.

Similarities to Analytics Automation

  • Automates end-to-end ML
  • Developed with advanced feature engineering capabilities

Limitations

  • Requires specialized knowledge in data science and/or coding
  • Hard to understand
  • Model transparency
  • Little to no self-service data analytics implementation and use
  • Limited data prep capabilities
    –Handed off to someone else to solve
    –May require coding to prepare
    –Can be a time-consuming task outside of the platform
  • Deployment and implementation costs can be high
  • Lack of self-service and drag-and-drop options and capabilities for advanced analytics

The Analytics Automation Advantage

Provides advanced analytics for people with little to no data science background or experience, enabling the upskilling of your workforce and empowering a culture of analytics across the organization.

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Tool & Analytics Automation Comparison: BI/Visualization

BI and Visualization tools work in tandem with Analytics Automation. Data and insights can be gained through Analytics Automation and fed into a BI/ Visualization tool to help present the information. However, you wouldn’t want to use BI or visualization as your sole data analytics tool, as forward-looking information often requires technical expertise, time, and costs.

Similarities to Analytics Automation

  • Dataset analytics presented as interactive graphs, charts, maps, and dashboards
  • Easy access to real-time data
  • Automates reports into visual formats and files

Limitations

  • Lack of customization outside the program forces users to ask outside teams for access or data enrichment
  • Time-consuming process delays ROI
  • Only visualization or charts and tables
  • Harder to ensure the reliability of projections without access to no-code analytic models — usually requires technical staff to implement; extra time
  • Little to no self-service data analytics implementation and use
  • Limited capabilities, including lack of ML, AI, and predictive and prescriptive analytics

The Analytics Automation Advantage

Provides for the analysis of huge volumes of data that can easily be exported and displayed through Tableau. Alteryx can also quickly surface the most important insights and reveal hidden signals that would have gone unnoticed in traditional visualization tools.

Uses Alteryx to Capture and Visualize Data
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Tool & Analytics Automation Comparison: Data Science Platforms

Incorporate machine learning, AutoML, and AI, but require specialized training such as R and Python coding.

Similarities to Analytics Automation

  • Integrates with libraries, tools, and algorithms
  • Supports batch scoring
  • Provides ML, AI, and predictive and prescriptive analytics capabilities

Limitations

  • Requires specialized knowledge in data science and/or coding
  • Little to no self-service data analytics implementation and use
  • No spatial analysis or third-party data enrichment — requires specialized training or technical staff to implement
  • Data preparation is less accessible to organizations; requires code libraries and functions
  • Creates data silos and has limited data catalog for collaboration
  • Limited to people specially trained in data science; limited amount of people trained in this

The Analytics Automation Advantage

Converges the benefits of a range of tools into one self-service, no-code (and low-code) data analytics platform capable of performing almost any type of data analysis — all focused on business outcomes.

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Transform Your Business Outcomes With Analytics Automation

The companies using Analytics Automation have realized benefits across lines of business in multiple industries. They’re using it to transform their business models and their culture.

Imagine what you could do with automated analytics. Imagine what you could do with a platform that allows you to learn. Imagine what you could do if the world of data analytics was suddenly put in your hands, or the hands of your team, and you could find the answer to anything you wanted.

Or, instead of imagining it, you could make it a reality.

Unleash The Alteryx Analytics Automation Platform

Shift your focus from rear-facing reports to automated insights with the only platform that puts people and business outcomes first.

Book a Demo

 

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