Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Teami

Strategies For the Game

How to Win at Big Data Analytics and Double the Impact of Your Analytics Team

CITO RESEARCH

Introduction

Beginning the Journey: A Solution Built for AnalystsEmpower analysts to create analytic-ready data

The Power of Keeping a Clean HouseEnsuring data quality is critical

The Art of Data BlendingGain insight by blending multiple data sources

Automate Data Preparation to Increase ProductivityLeverage automation to achieve and maintain data accuracy

Build an EcosystemUse solutions that play well together

Conclusion

Contents

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Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Team1

Introduction

The data analytics game has changed. Not only do customers engage with companies more often and via more channels than ever before, but the speed at which data is captured and stored has significantly increased. At the same time, the window for action is smaller. With more data and a shorter window for action, analytics need to be done faster and often without human interven-tion. To make sense of all this data — and do so accurately — organizations need to rethink how they’re preparing and handling data at scale.

That’s not all. In order to help their organizations become data-driven, analytics teams must expand their purview of the customer. They must look across the entire customer lifecycle — beyond customer acquisition and retention — and think about onboarding, usage, and engagement phas-es. That means analyzing new data sources, such as CRM data for onboarding, sensor or product data for usage, and social media or survey data for engagement.

But in order to do all of this, business analysts need time: Time to meet with new business partners. Time to secure and understand new data sources. Time to learn new techniques, and time to explain those techniques and their results.

This CITO Research eBook serves as a playbook for finding this time and winning at the new game of data analytics.

You cannot win the new game of big data analytics if…

i Your business analysts solely rely on IT for data preparation.

i Your business analysts, data scientists, marketers, and other line of business experts cannot do their own data prep. If they have to describe what they want and have someone else do the work, it simply takes too long.

i You don’t have advanced technology for matching data. Changing the form of data is not enough. The rules for transformation and matching must work on a semantic level and should serve to validate data to improve data quality.

i Your techniques for transformations and match-ing are one-offs. The rules must become part of an adaptive, automated pipeline that is applied to new and updated data sets.

Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Team

Strategy 1: Empower analysts to create analytic-ready data

A new game means new positions for your players. Business users — data scientists, data stewards, and business analysts — must be able to get the data they need in a usable form without waiting weeks for IT. In other words, they must be empowered to create their own analytic-ready data. Essential to empowerment is a solution that business ana-lysts find easy to use, with a drag and drop interface that facilitates their work and doesn’t require the programming or scripting skills of a developer or data scientist.

What is analytic-ready data?What does it mean to make data analytic-ready? It means having a data struc-ture in place with key fields and normalized values. For example, there must be an agreed-upon definition for common elements, such as how you define a customer. It also depends on the type of analytics being used. Predictive algorithms generally require a flat file with a target variable, so making data analytic-ready for prediction means that data sets must be ready for ingestion into those algorithms. It is also imperative to select the right predictive tools for the job. Businesses that overlook this critical step end up with the right answer to the wrong problem, and make decisions based on misleading results. Analytic-ready data is also critical for repeated analytic processes, like reporting, dashboards, and descriptive statistics.

Time is wasted and errors are made when analysts have to explain what they want to someone else who then has to interpret it. Business analysts need to express complex strategies for fixing problems with data without having to learn a programming lan-guage or relying on IT. Even in advanced environments like a Hadoop data lake, visual-based workflow solutions allow business analysts to get data on their own as easily as if they were uploading a spreadsheet.

The result: IT is no longer the bottleneck in the data analysis process. Instead, IT is charged with data gov-ernance, granting permission to access data, and creating and supporting analytic sandboxes and data lakes as needed. And business analysts have the data they need to get to work on creating analytic-ready data.

Beginning the Journey: A Solution Built for Analysts

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Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Team

Strategy 2: Ensuring data quality is critical

Athletes pride themselves on perfecting their techniques with precision. In the same way, data quality is a key success factor for data-driven organizations. Customers expect you to follow them across all touchpoints, and poor data quality is money down the drain because the data either can’t be analyzed or delivers incorrect results. On the other hand, clean data leads to incredible efficiencies.

A study by 451 Research of 200 C-level and senior IT leaders found that only 40% of C-level executives and data scientists are “very confident” in their organization’s data quality and yet 94% recognize the impact poor data quality has on business outcomes.

The ultimate goal is data precision, or, as your customers see it, getting it right. Data precision requires both data quality efforts from everyone who works with data as well as data stewardship, a higher level function ensuring that data is accurately captured, responsibly used, and definitions are agreed upon organization-wide.

Cleaning and validating data Business analysts should be empowered with solutions that clean and validate data as much as possible. Not only should solutions standardize data elements such as phone numbers and ZIP codes, which are often formatted differently depending on the source, but they should also perform data validation, ensuring that addresses, states, and postal codes are, in fact, correct. A step beyond this is enriching data with external information. Examples include geocoding (which is critical for spatial ana-lytics and real-time offers for mobile app users) or adding demographic information such as household income, age, presence of children, and the like.

Deep data quality requires powerful matching technology that uses the appropriate technique for the data at hand. Fuzzy matching should be used to identify potential duplicates in a data set (customers Al Green and Alan Green with the same address, for example), so that, if there’s a question, data can be re-viewed and merged. Such matching is also a key aspect of data blending, discussed next.

The Power of Keeping a Clean House

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Strategy 3: Gain insight by blending multiple data sources

Athletic training incorporates a variety of tech-niques to build endurance, strength, balance, co-ordination, flexibility, and agility. Similarly, big data encompasses not only more data but more varieties of data. In order to get value from multiple data sources, business analysts need a solution that can combine them in new, complex ways. This is the art of data blending.

Gone are the days when sophisticated data blending meant combining transaction logs with survey data. Today, analytics increasingly involves combining nu-merous data sources. In fact, according to Ventana Research, 92% of organizations have 16 to 20 data sources, and 80% have more than 20 sources. The ability to blend multiple data sources is essential for creating a 360-degree view of the customer and pursuing cross-channel opportunities.

Data blending involves combining data sources into a single data flow that can be used by multiple stakeholders.

Data blending is done using a combination of the business analyst’s savvy about data sources along with a solution that blends data intelligently, even if the data is in radically different formats (transac-tional data, customer data, interaction data from apps or websites, and sensor data, for example).

The Art of Data Blending

A step beyond blending is intelligent aggregation according to business rules. It might involve aggregating all the supplies, parts, and equipment used in a hospital. Aggregation also provides a deeper look into what is happening with consumers.

Consider this scenario: A large grocery chain was about to cancel a mailer due to a low response rate. Someone on the business team assumed that customers were sharing coupons due to the fact that customers in the control group (who didn’t receive the mailer) were redeeming the coupons, but that wasn’t it at all. Other people in the recipient’s household were using the coupons. The error was a result of poor householding aggregation. When the analytics were adjusted to account for accurate householding, there was a 400% difference in performance. Real-time analytics proved that the campaign was highly successful and worth repeating.

Smart data blending positions analysts to extract competitive advantage from big data. With large vol-umes of data from multiple sources, much of what gets into the system is noise with no real information content. Analytics helps identify the signal from the noise. Retail analysis might combine transactional logs associated with a loyalty card, data from a survey on a sales receipt, and traffic data generated from beacons at the store to determine not only dollars per person but dollars per second shopped — an underutilized metric in retail.

Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Team5

Strategy 4: Leverage automation to achieve and maintain data accuracy

Winning teams practice until their plays are second nature. Winning analytics teams take this one step further by automating important data flows, so that they can increase the amount of time spent on analytics and dramatically reduce the time spent on data preparation. The more you automate the data preparation process, the more time analysts have to do the work they were hired to do — and the more successful you’ll be at this new game of data analytics.

Rinse and repeat Business analysts need solutions that empower them to make their data sets analytic-ready, enabling them to assemble a variety of data sources using an intuitive drag and drop interface. But it doesn’t stop there. So much of analyt-ics is based on repeating the same type of work, refreshing the data set with the latest signals from social media, recent interactions and transactions, or geographic indicators (for example, if the customer is using an app on vacation and offers must be adjusted in real-time accordingly). The only way to handle such scenarios is to enable data preparation to be both easily set up and then automated, refreshing data on schedule or on demand to meet business require-ments. And of course automation must be easily modified to incorporate new data sources as they become available.

Data preparation — with high quality, blended data — should become an automated pipeline, a data supply chain that keeps continuing flows of data in top shape. Further-more, it’s a pipeline that has to scale with data growth so that more data really can mean more value.

Automation is the only viable way to handle the massive influx of new, changing data that must be processed each day. There simply aren’t enough people with the required expertise to handle all the tasks involved; automated data preparation is a must for businesses to effectively compete in today’s world.

Automate Data Preparation to Increase Productivity

Strategies for the Game: How to Win at Big Data Analytics and Double the Impact of Your Analytics Team

Build an Ecosystem

Strategy 5: Use solutions that play well together

Assembling a winning team is more important than recruiting a single star player. As a trend, software is evolving away from single-vendor platforms to systems that plug into multiple inputs or outputs. This is important because the deliverable coming out of any analysis is no longer a number, or even insight based on a number, but an action. You have to make sure the business can make a new decision or take a new action because it has insight based on a number.

Deep integration is keyA solution should offer deep integration that enables it to handle many things as opposed to just doing one thing. This allows you to pick best-of-breed prod-ucts and use them together. For example, for data blending, you want a solution that can take data that comes in from a variety of sources — the database, the weblogs, the survey house, the call logs from the call center, and so on. The best way to do that is to connect to those sources through deep integration. Deep inte-gration also allows you to push a file out to multiple users and in multiple formats, and to push data back and forth between tools such as Tableau, PowerBI, Qlik, and others. Automation is key to managing all of these integrated processes repeatedly and at scale.

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The new strategy for the game requires the ability to handle more data and more sources at scale to support contextual precision in execution. To win, organizations need a solution that offers high performance for data as growth scales, that blends many sources, and that offers sophisticated strategies for ensuring data quality and precision.

Business analysts must be equipped to prepare data faster without specialized coding skills. They need to make data analytic-ready by connecting to data wherever it is and bringing multiple sources together, transforming and cleaning data along the way so that a deeper level of analysis can be performed in their preferred analytics tool.

There is no question that organizations face explosive data growth as well as a proliferation of data sources. The only way to manage this growth is by empowering those with domain knowledge to do data preparation with smart tools.

CITO Research recommends RedPoint Data Management for automating data preparation at scale and believes that the semantic matching and aggregation capabilities of RedPoint set it apart.

RedPoint Data Management offers an integrated solution that supports businesses with a complete toolset to manage big data at scale, from data intake through cleans-ing, householding, analytics, prediction, and automation.

To learn more about RedPoint Data Management and read case studies that show RedPoint in action, visit redpoint.net.

Conclusion