On big data and its business impacts

Big data has a lavish potential to change the way people run businesses. This year, Statista published some encouraging success rates of enterprise big data projects. The industry-leading companies all over the world see a range of benefits brought in by their big data initiatives, from decreasing expenses to creating a data-driven culture.

Once the progress of data analytics made it possible to analyze large data sets at great speed, big data started to influence internal processes of many businesses. It can determine how decisions are made, strategies are created, and customer relationships are sustained.

In this article, we deliberate on how big data impacts businesses today and outline the major prerequisites for big data to bring benefits.

Big data use cases in retail and e-tail

Competitors in online and offline retail bite at one another trying to win customers. To stay at a safe distance, companies need to provide a comparatively better customer experience. Big data gives retailers new ways to stay ahead and innovate.

Personalized customer experience

Big data is now one of the critical aspects of the digital customer experience. By collecting data from multiple channels, such as social media, call logs, store visits, browsing history, and other, retailers can get a full view of their customers and fine-tune their operations accordingly, from marketing to customer service.

Such data assets are available not only to Amazon-like giants. Smaller brick-and-mortar businesses can leverage big data to stay competitive too, says Jessica Smith from aifora, a product development company. “Traditional brick-and-mortar have long struggled to collect data from multiple sources, but the advent of new technologies such as RFID, NFC, AI-cameras, people counters, etc. now enables them to collect far more data on their customer's buying journeys.”

She suggests, data sharing platforms or data marketplaces enabled with data fabric architecture solutions might be the answer to the data-collection challenge. “By anonymously sharing their data on one central platform, smaller retailers could build up a big data set, from which actionable insights can be drawn for each individual retailer. On their own, these retailers would not have sufficient data to draw meaningful conclusions.”

Product development

You can anticipate customer demand by using big data for uncovering trends such as seasonal demand, promotion success among various customer segments, popular complementary products, and more. Using these insights, it’s possible to build models for launching new products and services.

Upselling

By feeding big data to machine learning algorithms in retail that power recommendation engines, it's possible to serve personalized recommendations even to anonymous surfers. This way, visitors are more likely to find exactly what they want, as well as purchase more.

In-store experience

Instead of giving way to online stores, brick-and-mortar retailers can tap into IoT and big data to provide unmatched in-store experiences.

Offline businesses can analyze data from mobile apps, online and offline sales, customers’ locations and in-store behavior and use the insights to augment offline experiences, optimize store design and merchandizing, as well as encourage repeat purchases.

Big data use cases in healthcare

Healthcare services extensively use big data for many purposes, from clinical research and better care to building patient engagement platforms.

Genome research

Researchers now have a huge layer of data that helps them identify disease genes and biomarkers pointing to health risks.

Quality of care

For providers, each patient goes with a number of accompanying records. Big data is what creates a full view of each patient from multiple data sources and formats. In this environment, each doctor has access to a complete profile, whatever path the patient takes in their health and treatment journey.

Insurance fraud detection

Healthcare claims usually consist of multiple associated reports with records kept in different formats. This makes it difficult to apply insurance programs accurately and spot fraud timely.

Big data analysis helps to detect potentially fraudulent cases by spotting suspicious behavior and running checks much faster, compared to long waiting with traditional fraud detection models.

How to prep big data for big results

The high success rates of predictive analytics initiatives based on big data prompt businesses to allocate budgets for the tools that are able to process large data sets using machine learning algorithms.

As a matter of fact, the market for big data analytics apps is projected to grow from $5.3B in 2018 to $19.4B in 2026:

This evident upward trend shows no sign of stopping, but adopters still need to come well-equipped for such data-centered transformations. Big data does need to be harnessed, so we came up with this checklist for you to understand how prepared you are before you embark on your big data journey.

1. Get your infrastructure ready

In order to avoid bottlenecks, such as data processing speed and capacity, you need to have the appropriate network, data storage, and processing infrastructure.

The infrastructure should consist of the following layers:

• The data layer, to collect and store data.
• The integration layer, to extract data from multiple sources, to be transformed and loaded for analysis further.
• The processing layer, to process data for data scientists and analysts to make sense of it.
• The analytics layer, to analyze actionable data for trends and insights.

2. Keep your data clean

This may sound trite by now, but poor data quality is still taking its toll on businesses worldwide. According to Gartner, bad data costs businesses $15 million per year.

With big data, mistakes just get bigger. Irrelevant, duplicated, missing, incorrect, mistyped, or poorly integrated data affects all business sides that rely on data by their nature: human resource management, customer relationships, supply chains, finances, compliance, and more.

Poor data quality may result from human error, malfunctioning machines, or data being transferred incorrectly and thus becoming corrupted. Even minor errors in big data processing can have a detrimental impact if they influence decision-making or affect customer satisfaction.

One way or another, poor-quality data will show its teeth, and there’s no way to go about it except by investing in master data management (MDM) and data cleaning.

As an umbrella term, MDM includes a range of automated processes that serve the goal of delivering a single point of truth and reference. In the organizational setting, it is a central hub that accumulates data from a range of sources and then shares it between other internal systems without creating duplicates.

You can notice the potential problem here. While there is only one version of data in use by everyone, if this data is incorrect, errors will start snowballing from one data user to another. To prevent this, data cleansing and validation processes should be in place, and it’s not only about having software to perform this task. It’s also about data governance policies and user training sessions on how to fill in, audit, and process data correctly.

Be critical to your data and don’t just assume your data is correct. If data analysis results seem unexpected or suspicious, check the data validity by searching for and fixing errors. You should consult data analysts for an explanation or visualize big data to find extreme outliers and their causes. When data is transferred, be extra aware of possible errors or corruptions.

3. When migrating, do it wisely

In some cases, poor data quality is the direct consequence of a migration project gone wrong. For example, now migrating to the cloud is one of the biggest trends, yet cloud computing poses a few risks in its own right.

Data migration failures can result from poorly documented legacy systems, lack of well-described project requirements or thorough testing practices, but the outcome is likely to be the same. That is fractured, lost or incorrect data, which sends us back to the first point above.

To avoid this scenario, data migration projects need to involve specialists from all departments, including real data users. These user groups should collaborate with the data migration team made up of techies and executives, who will guide the project toward meeting the business requirements.

Strategy is also important. That’s why it’s necessary to allocate powerful system resources to support data migration and map out steps for each stage: before, during and after the migration.

Here’s your possible action plan:

1. Before migration, check the quality of existing data and validate business rules, redefining them if necessary.
2. Choose a fitting strategy and draw a careful roadmap based on the migration scope and budget.
3. Migrate in logical iterations while sticking to a realistic timeline.
4. Add ongoing data testing and evaluation to every project lifecycle stage.
5. Don’t skip validation of the migration results by both tech specialists and your data users.

4. Don’t leave self-service BI users on their own

Self-service BI tools bring in enormous opportunities to run ad-hoc big data analysis, minimize requests to IT departments, and give business users a better visibility into performance and productivity.

These benefits drive enterprises to make self-service BI applications their highest strategic priority. However, some of the users of advanced and predictive analytics, like business, financial and marketing analysts, won’t necessarily succeed when dealing with such applications.

The first pitfall is in the lack of role-tailored customizations. Part of the self-service BI success is having readily comprehensible interfaces and dashboards based on end users’ needs and preferences. Yet, these users can be the ones left out of the picture when the actual system is developed and deployed. Skipping the stage of interviewing end users and incorporating their feedback upfront might keep user buy-in down simply because the users will find reports and layouts irrelevant.

Another potential loophole is in low technical proficiency of self-service BI users. While analysts may know their job well, it’s unlikely they can start using a completely overhauled analytical platform right away. Counterbalancing this risk requires investing into user training in the form of typical demos and ongoing learning sessions, aimed at educating the BI workforce about technical implications of self-service BI systems.

To cut it short, it’s better not to overestimate your users’ technical skills but prevent productivity losses upfront.

5. Overcome internal bottlenecks

At large enterprises, different data types are scattered across departments. They are stored for different purposes and owned by different teams that often don’t communicate as efficiently as they should. Sometimes, one department stays unaware of the data located at another department, and therefore can’t take actions on it. This means overlooking a tremendous value that such cooperation could bring.

Such internal bottlenecks can be caused both by the lack of cross-departmental collaboration and poor system interoperability. Either way, remember that departmental division and information silos should not intervene with data dissemination throughout the enterprise.

A good starting point would be to bring together employees in charge of enforcing shared data policies in their respective teams. You could rely on your high-rank professionals who could motivate the rest of the organization to minimize bottlenecks and lags in data delivery.

Speaking of system interoperability, this should be addressed at the technical level, to provide accessibility that would satisfy security and efficiency standards. First, it takes mapping out data consumers to create a role-based access model with well-defined user rights. This would help you avoid data privacy issues and any cases of intentional or unintentional data misuse. Second, maintaining common data formats is key to your ability to use this data across internal systems.

The steps to overcoming internal bottlenecks include but are not limited to the following:

• Filter your data according to users’ roles, areas of expertise, interests, and responsibilities, to round up the volume of data they get to see and deal with.
• Prioritize valuable data by relevance to users within these specific roles.
• Make sure you distribute the latest data as soon as it becomes available, preferably through an alert system urging users to check their dashboards and analytical tools at once.