Can data analysis improve business decisions? (2023)

Market and customer insights are essential for business success. But there have always been challenges in gaining these insights. In today's digital age, you need a data analytics solution that integrates the best data management and analytics capabilities to quickly and easily access data and analyze the information you need, when and where you need it.

How can data analysis improve business decisions?

Deriving specific metrics or key performance indicators (KPIs) from data can be difficult. With data scattered throughout the enterprise, integrating information in a timely manner can also be problematic. Obtaining the information or insight your business needs to compete often takes a great deal of time and effort.

This is usually due to a probable defectAnalyseCapabilities. The data is readily available; However, no tool is available that provides quick access. In this case, business or data analysts could perform a quick self-service visualizationand analysis. Again, the data is often scattered, meaning the team must first manually collect the data before they can even begin analysis.

For example, due to the use of multiple sales applications, companies may have access to multiple data sources, including financial or marketing data extracts in CSV or Excel file format. They can even extract additional data that they have received ad hoc from other places. However, before any analysis is performed, the data must be combined, likely by attempting to use a spreadsheet as a database and then creating metrics or analysis from it.

This data collection process is much more difficult and time-consuming than the actual data analysis. And since it's also very manual, it's not repeatable; Therefore, if a retest is required three weeks later, this difficult and time-consuming process must be repeated.

This approach also creates a data consistency problem. Too often colleagues share a spreadsheet that is updated over time. As a result, the original spreadsheet is out of sync as different teams used different versions without anyone accessing a common current source. Combine this problem with cross-version formula errors and broken links inherent in spreadsheet sharing. All the typical problems associated with spreadsheets come into play here, but even more so when it comes to using a spreadsheet as a makeshift database.

There are also governance and security concerns. For team members responsible for financial planning and analysis, emailing critical financial information in spreadsheets or sharing it through SharePoint (or another collaboration tool) are risky security practices that could expose your organization to cybercrime.

What is self-service data prep?

To start leveraging data analytics for their business, companies are advised to first automate some of these processes through self-service data prep. This is an integrated and built-in feature of analysis tools that documents and automates the process to make it repeatable, significantly reducing analysis time and results.

commonstandalone solution, data-savvy business analysts can create a secure and shareable data repository in minutes and with just a few steps. Businesses can then leverage the self-service data prep capability in the cloud analytics platform to not only automate the data prep process, but also automatically populate a secure and shareable data repository. When the data is updated, everyone will see those updates as they are made, solving the problem of data security and consistency.

From a governance perspective, a centralized data and analytics team can see what data, transformations, metrics, reports, and analytics are being used, meaning they can all be tracked, including ad hoc datasets, within and across organizations. Frequently used data and datasets can be integrated with a departmental or enterprise data warehouse and metrics, as well as standard dashboards and reports. Isolated ad hoc processes are integrated with departmental and corporate processes, providing greater consistency, access and efficiency.

History of data analysis and technology roadmap

Historically, comparing statistics and analyzing databusiness ideasIt was a manual exercise, often time consuming, with spreadsheets being the main tool. Beginning in the 1970s, companies began using electronic technologies, including relational databases, data warehouses, machine learning (ML) algorithms, web search solutions, data visualization, and other tools with the potential to facilitate, speed up, and automate the analysis process.

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However, with these technological advances and increasing market demand, new challenges have emerged. A growing number ofCompetitive and sometimes incompatible data management and analytics solutionsAs a result, technology silos emerged, not only within departments and organizations, but also with external partners and vendors. Incidentally, some of these solutions are so complicated that they require technical expertise beyond the average business user, which limits their usability within an organization.

Modern data sources have also surpassed the ability of traditional relational databases and other tools to input, search, and manipulate large categories of data. These tools are designed to process structured information such as names, dates and addresses. Unstructured data generated by modern data sources such as email, text, video, audio, word processing and satellite imagery cannot be processed or analyzed using traditional tools.

Accessing a growing number of data sources and determining what is valuable is not easy, especially since most of the data produced today is semi-structured or unstructured.

What are the best types of data analysis?

Which type of data analysis is best suited for a company depends on its level of development. Most companies are probably already using some form of analytics, but it typically only provides information for reactive business decisions, not proactive ones.

Organizations are increasingly using sophisticated data analytics solutions with machine learning capabilities to make better business decisions and determine market trends and opportunities. Organizations that don't start using data analytics with forward-thinking, proactive skills may experience poor business performance due to an inability to uncover hidden patterns and uncover other insights.

Four main types of data analysis

1. Predictive Data Analysis

predictive analyticsis possibly the most widespread category of data analysis. Companies use predictive analytics to identify trends, correlations and root causes. The category can be divided intopredictive modelingmiStatistical ModelingπŸ‡§πŸ‡· However, it is important to know that both go hand in hand.

For example, a Facebook t-shirt ad campaign may apply predictive analytics to determine how conversion rate correlates with a target audience's geographic area, income range, and interests. From there, predictive modeling can be used to analyze statistics from two (or more) audiences and provide potential sales numbers for each demographic.

2. Prescriptive Data Analysis

Prescriptive Analytics ist woImibig dataThey combine to predict outcomes and determine what actions to take. This analysis category can be divided intoimprovementmirandom testπŸ‡§πŸ‡· Using advances in ML, Prescriptive Analytics can help answer questions like "What if we tried that?" to answer. and "What is the best action?" You can test for the right variables and even suggest new variables that offer a higher chance of a positive result.

3. Analysis of diagnostic data

While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in running your business. Diagnostic data analysis is the process of examining data to understand the cause and event or why something happened. Techniques such as disaggregation, data discovery, data extraction, and correlations are commonly used.

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Analyzing diagnostic data helps answer why something happened. Like the other categories, it is also divided into two more specific categories:Discover and warnmiadvice and in detailπŸ‡§πŸ‡· Queries and drilldowns are used to get more details out of a report. For example, a sales rep who closed far fewer deals in a month. A breakdown may show fewer business days due to a two week holiday.

Detection and alerts notify you of a potential problem before it occurs, e.g. B. A warning about a reduced number of staff hours, which can lead to a decrease in the closed deals. You can also use diagnostic data analysis to "uncover" information such as the most qualified candidate for a new position at your company.

4. Analysis of descriptive data

Descriptive analysis is the backbone of reports: it's impossible to haveBusiness Intelligence (BI)tools and panels without it. It addresses the fundamental questions of β€œhow many, when, where and what”.

Again, descriptive analysis can be divided into two categories:unprepared for itmiready-made reportsπŸ‡§πŸ‡· A canned report is a report that has been designed in advance and contains information on a specific topic. An example of this is a monthly report sent out by your advertising agency or team that breaks down the performance metrics of your recent advertising efforts.

Ad hoc reports, on the other hand, are designed by you and are usually not planned. They are generated when a specific business question needs to be answered. These reports are useful for getting more detailed information about a specific query. An ad hoc report may focus on your business profile on social media, examining the types of people who like your Page and other industry pages, as well as other demographic and engagement information. Its hyperspecificity helps convey a more complete picture of your social media audience. You most likely won't need to see this type of report a second time (unless there's a big shift in your audience).

Business oriented insights and how to deal with a fast moving market

In an ever-changing business environment, it can be difficult to predict your next move. This is where data analysis comes into play. With quick access to data from every team and across the organization, you can make better decisions by gaining deeper insights into:

  • Who are your customers and how do you reach them?
  • The market, including competitors.
  • what happened in the past
  • What is happening now
  • What does the future hold for your company?

Use data to make informed decisions

If you were dealing with a single customer sitting at your desk, gathering the necessary information and acting on it would be easy. But how many companies have only one customer? To get the typical customer pool, you would need to multiply that customer by a hundred, a thousand, or a multiple. Add in marketing and customer data provided in a variety of ways and from multiple sources, and you'll find that it can be difficult to get the information you need and know how to move forward. You need a data analytics solution that's up to the task.

What you should consider for your data analysis solution

If you want to build a more insight-driven organization, there are numerous data analytics products on the market today. Ultimately, the ideal solution offers modern analytics tools that are predictive, intuitive, self-learning, and adaptable.

To support all types of data usage in your organization, consider the following:

  • You want a single platform that integrates data management and analytics capabilities. This solution avoids the compatibility and accessibility issues of a legacy environment that offers multiple solutions for reporting, detection, analysis, and recommendations. Everything is integrated and included to simplify deployment and deliver business value faster.
  • A platform that resides in the cloud but can access data in on-premises and/or hybrid environments is essential. Fast and easy access to data and analytics empowers everyone in the organization to gain insights and make informed decisions.

An end-to-end analytics solution

Look for a solution that supports the entire analysis process, from data collection to information delivery to prescribed actions, with security, flexibility, reliability and speed.

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Enjoy all dates

Choose a solution that accesses and analyzes available data of any size and location from applications (including IoT), departments, third parties, structured and unstructured, on-premises and in the cloud. This solution simplifies data processing to uncover the true value of your data, uncover hidden patterns and relevant information to help users make informed, data-driven decisions.

Improve productivity and data integration

The ideal data analytics solution streamlines every step of your data workflow. This makes data and analysis processes faster. Built-in features like machine learning velocity model building. Efficiency is improved throughout the process, including data collection, information discovery, and improved decision-making.

Benefit from a single source of information

For reliable analysis, insight and results, data must be consolidated into a single source. This enables consistency and accuracy with a unified view of data, metrics, and insights.

Accelerate data insights

Are you looking for a solution with augmented analytics, e.g. B. embedded AI andmachine learning– to simplify, accelerate and automate tasks and give you the opportunity to access your market deeper and faster. Automatically collects and consolidates data from multiple sources and recommends new datasets for analysis.

Self-Service Analytics: Kostenlose IT

To realize its potential as a business tool, analytics must be democratized. That means having a solution that doesn't require IT support. Anyone in your organization with the appropriate permissions should be able to use it. The idealanalytics solution is designed for self-service, with point-and-click or drag-and-drop functionality and step-by-step guided navigation. Without IT support, users need to be able to easily upload and import data and analyze it from any angle.

Best-practice data analytics solutions give users the self-service ability to find, understand, control, and track data assets across the enterprise based on metadata and business context. This speeds time-to-value and makes it easier to find usable data. Data discovery, collaboration, and governance can be enhanced with custom annotations, labels, and business glossary terms.

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Analytics has the potential to give you a detailed picture of your business landscape. To get the most out of this potential, you need an intelligent solution that can automatically convert data into visual presentations. This allows you to identify and understand patterns, relationships, and trends that a table of raw numbers might miss. It also allows you to create data combinations to get new and unique information. Thanks to intelligent technology, this is possible without special training.

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Mobile Analysis

You want a solution that gives your employees access to the information they need on the go. But not all mobile analytics solutions are created equal. Consider a mobile analytics solution that not only offers voice-activated access and real-time alerts, but also offers advanced features to help your employees be even more productive.

These capabilities include building mobile analytics apps with interactive images from a phone or tablet without having to write code. Or imagine a solution that scans your digital footprint, knows you're attending an out-of-town meeting, and provides information to make the meeting a success.

Data analysis welcomes automation and autonomy

Millions of handcrafted spreadsheets are used across many industries including finance, science, and business. However, according to ZDNet,90% of all spreadsheets are incorrectaffecting your results. Cut and paste problems, hidden cells, and other errors have cost companies millions of dollars.

Traditional analytics solutions and processes can also cause delays in delivering the insights businesses need to make timely decisions. Data is often collected from multiple applications and platforms, requiring an enterprise department to: create extract, transform and load (ETL), connections and interfaces; transfer data from one database to another; monitor the quality of the data; and enter data into spreadsheets. All of these tasks can consume valuable time and resources.

Also, with traditional solutions and processes, you often need to be an IT or analytics professional to perform the analytics. It's not a self-service experience for busy executives who need month-end analysis. And that means waiting for the IT or analytics specialist to deliver what's needed.

Automating analytics processes and deploying them to the cloud can be a game changer for organizations of all sizes and industries. For example, a modern analysis solution with integrated AI and ML and aautonomous data warehouserunning on an autonomous cloud with self-protection, self-healing and self-optimization.

If you work with a modern analytics solution, everything can be automated. Identify a few parameters for what you want to see, what model you want to apply, and what column you want to predict, and then the solution takes over. Data can be ingested from multiple applications, platforms and clouds. It can be assembled, cleaned, prepared, transformed, and analyzed to make predictions, all automatically, speeding up processing and reducing the possibility of human error.

When you choose Oracle, you get a single, integrated platform that combines Oracle Analytics and Oracle Autonomous Database. It's a simple and repeatable solution with the best analytics and powerful autonomous data services. This means it removes roadblocks, brings data together into a single source of truth, and unleashes highly actionable insights quickly, making it an ideal data analytics solution to drive strategic business decisions.

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