As we enter a new decade, enterprises must identify proven solutions to accelerate customer service, market interpretation, and improved experiences. Advanced analytics and data interpretation are prerequisites to success.
In response, companies are adopting technologies utilizing artificial intelligence (AI) and machine learning (ML) to implement business intelligence (BI) and augmented analytics solutions.
We recently spoke with subject matter expert, Vitalii Bashun, about the benefits and pitfalls of smart analytics, which companies are best suited to implement an analytics approach, and his views on what lies ahead.
Google Cloud empowers users to leverage data in new ways and accelerate innovation. The serverless, managed approach removes barriers to performance, scalability, and availability requirements—allowing engineers to build the models and workflows needed for predictive insights.
To that end, smart analytics solutions using Google Cloud deliver both immediate and long-term benefits, including rapid insights, data security, and the increased productivity required for transformation at the speed of digital.
We recently spoke with subject matter expert, Vitalii Bashun, about the benefits and pitfalls of smart analytics, which companies are best suited to implement a smart analytics approach, and his views on what lies ahead.
MRA: Thanks for taking time out of your busy schedule to lend us your insights today, Vitalli!
VB: My pleasure, thanks for having me.
MRA: Smart analytics might mean different things to different people. What does smart analytics mean to you, and your work at SoftServe exactly?
VB: Smart analytics is something that goes beyond just simple statistics by providing deep insights based on company data. The company or enterprise may have undefined hidden patterns in data, in clients’ behavior, or in the system behavior itself. Those hidden patterns are unseen and, therefore, are unusable for business decisions. However, using advanced technologies like AI and ML, businesses can unlock that hidden data and reveal more in-depth trends, rather than just basic statistics.
MRA: So, we can sum up smart analytics as being deeper, more relevant, and actionable insights. That makes sense. So, let’s talk about benefits, problems solved, and long-term value proposition. What would you say are the immediate benefits to enterprises using smart analytics today?
VB: In two words: business advantage. Either your competitors already have an analytics solution, and you must keep pace with them—or you can easily create a distinct advantage by implementing a new solution. Analytics solutions allow businesses to use their information to make smarter decisions based on reliable data. Primarily, the company will immediately experience an improvement in their reaction speed, advance their service quality, and save money.
Quality improvements and technology optimization directly influence all aspects of the business, leading to competitive advantage and overall cost savings.
MRA: That sounds great. Do you have any examples or use cases you can discuss?
VB: An obvious example would be online shopping where a recommendation engine is used to promote additional products to customers. The benefits to the retailer are evident as it increases overall company revenue while also providing value to the customers. While the recommendation engine works well for e-commerce, it also works well for media and streaming companies. This provides not only improved viewing metrics but also improves the overall experience.
MRA: So, smart analytics has the power to make recommendations to improve business and drive sales. That’s beneficial, without question. But, let’s move on and address potential pitfalls. What should a company be aware of before implementing a smart analytics solution?
VB: Sometimes, a company comes to us and says something like “we want something cool” without a solid business plan or use case. For the implemented solution to be successful, we need to start with the goals and business needs. Without a clear objective and business criteria, measuring success is impossible. So, a company must begin by understanding what it wants to improve because data science and engineering teams can be costly if not utilized properly. That’s why working with a partner like SoftServe, on a secure cloud platform, like Google Cloud, will help companies identify clear objectives to realize the full value of a smart analytics solution.
MRA: That certainly makes sense. After objective identification, what is your recommended optimal approach?
VB: The next thing a company needs is clean data. Often, we see data of poor quality, that is often incomplete or in differing forms. To be successful, a company needs data that is suitable for consumption. Next, the company should understand what data it needs and how the data will be used. We use design thinking workshops to understand how end-users will work with the application analyzing motivation, what the user will do before working with the application, how they interact with the application during the use case, and what they do after (outcome). We use modern data engineering approaches to collect data from different parts of the organization, to clean data and control its quality, and to prepare data to serve analytical purposes. Design thinking is a way to understand the end-user and what type of analytics is beneficial to the company.
MRA: You would recommend most companies start with something like a design thinking workshop to understand the project first?
VB: Yes, because they need to understand the project before they can go deeper. Companies need to learn how end-users interact with data, and what the user will do with this data—which encompasses understanding the entire user experience. With this in mind, we can start our work understanding how to achieve the final objective.
Explicitly talking about Google Cloud, they already have many essential services in place, reducing the installation of additional requirements. Users can create algorithms, add in the data, all by using their services. Plus, they start to work immediately.
MRA: Are there companies that are better suited to implement a solution than others?
VB: If you don’t understand your business, and you don’t understand your strategy, a smart analytics solution might not be as successful for your business. Companies should always start with the strategic vision to improve or to transform their business. Then, a smart analytics solution will deliver insights on loss prevention, profit maximization, risk analysis, and highlights—often based on dates or regions. This allows leaders to assess how to prevent these problems in the future. Often for much smaller businesses or start-ups, a smart analytics solution is not necessary—possibly even overkill—as they may already have a better grasp on their customers and cost may be prohibitive in this case.
MRA: Can you talk a little bit about the technical side of smart analytics for clients? Are there any sort of technical aspects, including cloud, or those layers underneath smart analytics that our readers should know about?
VB: Of course. Smart analytics is not locked by the cloud, as it can be contained in an on-prem environment. For example, many banks still prefer to use their facilities for processing, and that’s fine. At the same time, we do see that moving to the cloud, especially Google Cloud makes it possible to start faster. You don’t need to buy additional physical servers or extra disc space. Users can open a laptop, create a cloud account, and start working straight away. So it’s like a potent business accelerator if you will.
MRA: What about other technical aspects, outside of infrastructure?
VB: When working with different clients, we’ve recently seen a lot of movement with cloud migration from AWS to Google Cloud. That’s often because Google Cloud typically offers better services in terms of machine learning and overall strategy in the way people create programs by writing code and algorithms. Previously people focused on writing programs, but the goal has changed to machine learning algorithms instead. Before, when you wrote a program, you also had to provide clear, step-by-step instructions for the program to know what to do. Now, machine learning allows users to create exact algorithms and predict their behavior, allowing users to create innovative things. However, some algorithms are quite complex to understand how they work, what data to feed it, and how to configure it. The optimization takes real experts to be able to manipulate properly.
MRA: With such a technical and in-depth process, it sounds like many companies may need to work with experts highly educated in this field.
VB: Yes, to create an optimal algorithm and verify the results is not something an average person could complete. This is another problem we solve at SoftServe—primarily using Google Cloud services. We’ve worked with many companies who spent money on internal projects with internal resources, only to abandon the project as it became too complex to complete. That’s why the SoftServe Data Science Team itself is built around eight PhDs and many highly educated employees with advanced educations in machine learning and mathematics. We’re extremely proud of our team and the smart analytics work we have created by using Google Cloud.
We’ve addressed the question of why a company must make smart analytics a priority. There is no longer a question of if or when only how and what’s next. Smart analytics integration is a must for any enterprise seeking to maintain competitive differentiation while achieving sustainable efficiency, security, and profitability. Leveraging the expertise of a cloud implementation, migration, and optimization partner can augment existing internal strengths while covering gaps in cloud-native talent or bandwidth. Contact SoftServe today and let’s talk about where you are in your app modernization journey—and how Google Cloud empowers companies to achieve and sustain digital transformation. #ForTheFuture
Benefits of smart analytics:
- Analysis and monitoring from vast amounts of data
- Near real-time action and response
- Advanced decision-making abilities for complex scenarios
- Improved employee and customer experience
- Reporting across periods, regions, segments, etc