Insights about the market and customers are essential for business success. But there have always been challenges in getting those insights. In today’s digital era, you need a data analytics solution that integrates the best of analytics and data management capabilities to quickly and easily access the data and analyze the information you need—when and where you need it.
The ability to derive certain metrics or key performance indicators (KPIs) from data can be difficult. With data scattered throughout an organization, getting integrated information in a timely manner can also prove to be problematic. Typically, getting the desired information or insights your business needs to compete often takes too long and requires too much effort.
This is often due to a probable lack of analytics capabilities. The data is readily available; but there is no available tool that provides fast access. If there were, data or business analysts could do rapid, self-service data visualization, and analysis. And again, the data is often scattered, which means staff must first manually gather the data before they can even start their analysis.
For instance, due to the use of multiple sales applications, businesses likely have access to several sources of data, including marketing or financial data extracts in a CSV or Excel file format. They may even pull in additional data that was obtained on an ad-hoc basis from elsewhere. Before conducting any analysis however, the data must be merged, most likely by trying to use a spreadsheet like a database, and then building metrics or analyses from that.
This data gathering process is much more difficult and time consuming than the actual data analysis. And since it’s also very manual, it’s not repeatable, so when new analysis is needed three weeks later, that difficult and time-consuming process has to be done again.
This approach also creates a data consistency issue. Far too often, coworkers share a spreadsheet that gets updated over time. As a result, the original spreadsheet becomes out of sync, since different teams have used different versions with no one accessing a common and current source. Compound this issue with formula errors between versions and broken links inherent to spreadsheet sharing. All the typical problems that arise with spreadsheets come into play here, but even more so when trying to use a spreadsheet as a makeshift database.
There are also governance and security concerns. For team members responsible for financial planning and analysis, emailing core financial information on spreadsheets or sharing them via SharePoint (or another collaboration tool) are risky security practices that could expose your company to cybercrime.
To get started using data analytics for your business, it’s recommended that organizations begin by automating some of these processes using self-service data preparation. This is an integrated and built-in capability of analytics tools that document and automate the process so that it is repeatable—greatly reducing the time to analysis and results.
With an autonomous solution, data-aware business analysts can spin up a secure and sharable data repository within minutes in just a few, simple steps. Businesses can then use the self-service data preparation capability within the analytics cloud platform to not only automate the data preparation process, but to also automatically populate a secure and sharable data repository. When data is updated, everyone will see those updates as they’re made, solving the data consistency and security issue.
From a governance perspective, a centralized data and analytics team can see what data, transformations, metrics, reports, and analyses are being used—which means they can all be tracked—including those ad-hoc datasets—within and across business functions. Datasets and data that are commonly used can be incorporated into a departmental or enterprise data warehouse and metrics, as well as standard dashboards and reports. Isolated, ad-hoc processes are integrated into departmental and enterprise processes, allowing even greater consistency, access, and efficiency.
Historically, comparing statistics and analyzing data for business insights was a manual, often time-consuming exercise, with spreadsheets being the go-to tool. Starting in the 1970s, businesses began employing electronic technology, including relational databases, data warehouses, machine learning (ML) algorithms, web searching solutions, data visualization, and other tools with the potential to facilitate, accelerate, and automate the analytics process.
Yet, along with these advances in technology and increasing market demand, new challenges have emerged. A growing number of competitive, sometimes incompatible analytics and data management solutions ultimately created technological silos, not only within departments and organizations but also with external partners and vendors. Incidentally, some of these solutions are so complicated they require technical expertise beyond the average business user, which limits their usability within the organization.
Modern data sources have also taxed the ability of conventional relational databases and other tools to input, search, and manipulate large categories of data. These tools were designed to handle structured information, such as names, dates, and addresses. Unstructured data produced by modern data sources—including email, text, video, audio, word processing, and satellite images—can’t be processed and analyzed using conventional tools.
Accessing a growing number of data sources and determining what is valuable is not easy, especially since the majority of data produced today is semistructured or unstructured.
The best type of data analytics for a company depends on their stage of development. Most companies are likely already using some sort of analytics, but it typically only affords insights to make reactive, not proactive, business decisions.
More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help determine market trends and opportunities. Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance lacking because they lack the ability to uncover hidden patterns and gain other insights.
Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling; however, it’s important to know that the two go hand in hand.
For example, an advertising campaign for t-shirts on Facebook could apply predictive analytics to determine how closely conversion rate correlates with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) target audiences, and provide possible revenue values for each demographic.
Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.
While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in guiding your business. Diagnostic data analytics is the process of examining data to understand cause and event or why something happened. Techniques such as drill down, data discovery, data mining, and correlations are often employed.
Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two more specific categories: discover and alerts and query and drill downs. Query and drill downs are used to get more detail from a report. For example, a sales rep that closed significantly fewer deals one month. A drill down could show fewer workdays, due to a two-week vacation.
Discover and alerts notify of a potential issue before it occurs, for example, an alert about a lower amount of staff hours, which could result in a decrease in closed deals. You could also use diagnostic data analytics to “discover” information such as the most-qualified candidate for a new position at your company.
Descriptive analytics are the backbone of reporting—it’s impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”
Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example of this is a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts.
Ad hoc reports, on the other hand, are designed by you and usually aren’t scheduled. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. Its hyperspecificity helps give a more complete picture of your social media audience. Chances are you won’t need to view this type of report a second time (unless there’s a major change to your audience).
In a constantly changing business environment, it may be hard to predict your next move. That’s where data analytics comes in. By quickly accessing data across teams and the enterprise, you can drive better decisions by getting deeper insights about:
If you were dealing with only one customer sitting across the table from you, it would be easy to gather the necessary information and act on it. But how many businesses only have one customer? To get the typical customer pool, you’d have to multiply that one customer by a hundred, a thousand, or many more times. Add marketing and customer data provided in a variety of ways and from diverse sources, and you’ll find obtaining the information you need—and knowing how to move forward—can be difficult. It requires a data analytics solution that is up to the task.
If you want to build a more insight-driven organization, there are plenty of data analytics products on the market today. Ultimately, the ideal solution offers modern analytics tools that are predictive, intuitive, self-learning, and adaptive.
To support all the ways that your organization will use data, here are a few things to keep in mind:
Look for a solution that supports the entire analytics process, from gathering data to providing insights and prescriptive actions—with security, flexibility, reliability, and speed.
Choose a solution that accesses and analyzes available data—of any size and in any location—from applications (including the Internet of Things), departments, third-parties, structured and unstructured, onsite, and in the cloud. Such a solution streamlines data processing to unlock the true value of your data, uncovering hidden patterns and relevant insights to help users make informed, data-driven decisions.
The ideal data analytics solution optimizes all steps in your data workflow. That makes data and analytics processes faster. Built-in capabilities, such as machine learning, accelerate model building. Efficiency is enhanced everywhere in the process, including data gathering, discovering insights and improving decision-making.
For trustworthy analytics, insights, and results, data should be consolidated into a single source. Doing so allows for consistency and accuracy with a unified view of data, metrics, and insights.
Look for a solution with augmented analytics—such as embedded AI and machine learning—to simplify, accelerate, and automate tasks, giving you the power to dig deeper and faster into your market. It automatically collects and consolidates data from multiple sources and recommends new datasets for analysis.
To realize its potential as a business tool, analytics needs to be democratized. That means having a solution that doesn’t require IT assistance. Anyone in your organization with the proper authorization should be able to use it. The ideal analytics solution is designed for self-service, with point-and-click or drag-and drop functionality and guided, step-by-step navigation. Without assistance from IT, users should easily be able to load and import data and analyze it from any angle.
Best practice data analytics solutions offer users the self-service capability to find, understand, govern, and track data assets across the enterprise based on metadata and business context. Doing so accelerates time to value and makes it easy to find fit-for-use data. Data discovery, collaboration, and governance can be enhanced with user-defined annotations, tags, and business glossary terms.
Analytics has the potential to give you a detailed image of your business landscape. To help make the most of that potential, you want a smart solution that can automatically transform data into visual presentations. This allows you to see and understand patterns, relationships, and trends that might be missed with a spreadsheet of raw numbers. It also lets you create data mash-ups to get new, unique insights. You can do that without specialized training, thanks to smart technology.
You want a solution that can give your people access to the information they need when they are on the road. But not all mobile analytics solutions are created equal. Consider a mobile analytics solution that not only offers voice-enabled access and real-time alerts, but provides advanced capabilities to help your people be even more productive.
These capabilities include creating mobile analytical applications with interactive visuals from a phone or tablet—without writing code. Or imagine a solution that looks at your digital footprint, knows you’re about to attend a meeting out of town, and delivers insights to help that meeting be a success.
Millions of manually prepared spreadsheets are used for diverse industries, including finance, science, and economics. Yet, according to ZDNet, 90% of all spreadsheets have errors that affect their results. Cut-and-paste issues, hidden cells, and other errors have cost businesses millions of dollars.
Traditional analytics solutions and processes can also cause delays in providing businesses with the insights needed to make timely decisions. Often, data is collected from multiple applications and platforms, requiring a corporate department to: create the extract, transform, and load (ETL), the connections, and the interfaces; transfer data from one database to another; look at the data quality; and enter the data into spreadsheets. All of these tasks can take precious time and resources.
In addition, with traditional solutions and processes, you usually need to be an expert in IT or analytics to conduct the analysis. It is not a self-service experience for the busy executive who requires end-of-month analytics. And that means waiting for the IT or analytics expert to provide what’s needed.
Automating analytics processes and putting the processes in the cloud can be a game changer for businesses of all sizes and in all industries. For example, a modern analytics solution with embedded AI and ML and an integrated autonomous data warehouse that runs in a self-securing, self-patching, self-tuning autonomous cloud.
When you’re working with a modern analytics solution, everything can be automated. Identify a few parameters of what you want examined, which model to apply, and which column you want to predict, and then the solution will take over. Data can be ingested from multiple applications, platforms, and clouds. It can be gathered, cleaned, prepared, transformed, and analyzed for predictions—all automatically, accelerating processing and reducing the chance of human-created errors.
Choose Oracle and you’ll get a single, integrated platform that combines Oracle Analytics and Oracle Autonomous Database. It’s a simple, repeatable solution with the best elements of analytics and powerful autonomous data services. That means obstacles are removed, data is brought together into a single source of truth, and highly actionable insights are unlocked—fast—which makes it an ideal data analytics solution to guide strategic business decisions.