What is Analytics-as-a-Service (AaaS)?
According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to reach $101.29 billion by 2026. A worldwide leap of big data has compelled businesses to invest in data analytics tools and services.
How can your business benefit from AaaS?
If your business falls under any of these categories, you need AaaS.
- Tech
- Healthcare
- Legal
- Finance
- Travel
- Insurance
- Agriculture
- Energy
- Mining
- Education
- Telecom
- Retail
A common and relatively relatable example of an industry that has embraced AaaS is retail. Daily, this industry produces humongous amounts of data from thousands of touchpoints such as websites, mailing lists, in-store purchases, mobile POS, and more. The data must be regularly analyzed to predict consumer behaviors and boost revenues.
On-premise analytics for these companies can be expensive, as they would require teams of data scientists. Tools like CRM suites, which simplify the process of onboarding data from multiple streams, can help companies get organized and share their data more effectively. And reduces the cost of analysis and offers quicker comprehension, better insights, and more agile business processes.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Four tips to get started with analysis as a service
If you are looking to offer analytics-as-a-service, here are four tips for beginning with.
- Try to understand what your customers want.
First, service providers need to determine what type of data is most likely to be analyzed by the business and what results they want to achieve through the analysis. Then accordingly, software must be fine-tuned to provide customers with an optimal experience.
The data transfer process must also be easy and transparent. Customers must get guarantees that they can withdraw and utilize their data at any time, probably for internal analysis or forecasting.
The files must be supported in standard formats such as XML, JavaScript Object Notation (JSON), etc., to facilitate data sharing between customers and suppliers. Lastly, costs must be reasonable and attractive while allowing customers to customize the services as required.
- Make the right connections with your clients’ internal teams.
While it is important to analyze the parameters and assess the clients’ requirements when designing their solution, developing strong connections with your clients’ internal teams is equally critical.
The clients’ team members on board with the services provider should have appropriate knowledge regarding data analytics. This will help melt the communication barrier between both parties and help the teammates understand the service offerings better. They can also suggest to their superiors if the offered solution needs additional or subtraction services.
The service provider should also realize that disclosing the process to irrelevant team members would cost time and resources; hence they should build the right connections with the right people from the very beginning.
- Demonstrate your value gradually.
Once a relationship is established with a client and relevant team members, it is necessary to identify the point of access to the data. Finding a way to access, transfer and analyze data is a challenge. A quick look at servers and infrastructures is not enough to gain actionable insights.
By developing an efficient data transfer process and suitable customized solutions, the service provider will prove the worth of its services. Sometimes, customers are only looking for a provider to leverage their computing power and data storage system, but the AaaS service provider offers much more. It can uplift their data analytics game while organizing, analyzing, and visualizing data to deliver actionable insights.
- Identify the most profitable customers.
The smart service providers should also be focusing on growing their business and sales with the most profitable customers while finding ways to manage less profitable ones. The key is to analyze each customer: how much they spend, how many resources their business ties up, and, most importantly, the profits you make on their business.
An infrastructure tailored to the client’s needs makes the service provider a better fit. For companies, data analysis is about improving business. Suppliers that understand this will more easily enter into profitable partnerships.
Ultimately, you want to change how you deal with customers at each level: least profitable, most profitable, and average. Work hard to convert average customers into more profitable customers and least profitable customers into average customers. No matter what, take great care of your most profitable customers, as they are the lifeblood of your business.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
3 Reasons to Consider Analytics-as-a-Service for your Business
In addition to traditional data sources, the rise of social media platforms and increased use of the internet have led to an abundance of data. Companies that were quick enough to convert this explosion of data into business insights have boomed in the last decade.
Interestingly, a study by HFS research found that while 85% of enterprises view smart analytics as pertinent to success, less than 1 out of 5 enterprises felt they could handle data analytics at a large scale. A feasible option is to leverage the benefits of analytics-as-a-service (AaaS).
The new set-up that AaaS provides allows clients to use a particular analytics software for as long as needed and can be more cost-effective and less labor-intensive when compared to the traditional way.
- Impact of Analytics-as-a-Service
Analytics-as-a-Service (AaaS) is becoming a valuable asset for many businesses. Organizations that need to carry out more analytics may need many more additional servers, amongst other expensive hardware. This also means they need to hire more staff in their IT departments to implement and keep these analytics programs instead. So, AaaS helps to bypass these new costs and business requirements.
With access to tools, technology, and skilled resources, AaaS effectively processes massive volumes of data and transforms them into actionable insights. While cost can be a driving factor in choosing an analytics solution, consider the impact that AaaS has on the following key areas.
- Access to insights for C-Suite
With rapid changes in global trends, regulations, market dynamics, and customer preferences, top executives need to be equipped technically as well. Although the teams might include data scientists and data analysts on the front end, the decision-makers should also use all this information to their benefit. While it is impossible to respond to every shift in the market, failure to keep up with key changes can prove costly.
C-Suites can make critical decisions by combining insights and trends by adopting a data-driven culture. However, building these capabilities in-house is not only expensive but time-consuming. With analytics-as-a-service (AaaS), management teams are empowered with live insights while maintaining a low cost of ownership.
For instance, the company has tested a new product in the market. The data analyst team presents insight after data collation from 3rd party sources where individuals publicize their thoughts and opinions. The C-Suites must be technically able to read between the lines and numbers to decide whether the product should be launched on full-scale. Later on, if the market share for this product grows, they can anticipate the market demands to provide the product before it is requested, ultimately staying competitive.
- Crafting Customer Experiences using Customer Data Analytics
With the rise of digital, customer touchpoints have exponentially increased. This has allowed us a micro-level view of customer preferences and trends like never before. Customer experience, satisfaction, and associated returns on investments engage C-suite leaders across industries.
But without the ability to turn customer data into insights, an organization gets a fragmented view of the customer. This leaves them far behind their competitors in their bid to provide personalized services. Using AaaS, organizations can overcome issues of such data silos to tell a unified story in a scalable manner, thus enhancing the customer experience with analytics-driven solutions.
For example, a business is struggling to maintain service levels and reduce cost per order without a centralized order management report from each of its regions. By deploying AaaS, they can fetch automated reports with standard and common KPIs, thereby improving their service levels and leading to visible annual savings.
- Empowering Employees with Data
Analytics enables organizations to respond to market events with a high level of precision. The use cases are endless, from intelligent order management for the retail industry, resource allocation for hospitals based on demand, and customized marketing campaigns.
However, the reality is that very few organizations can provide their employees with access to predictive insights. This, in turn, affects the ability of employees to deliver value as they lose precious time mining and analyzing data on spreadsheets.
With an AaaS provider, organizations can quickly make intelligent insights accessible to every decision-maker, fostering transformation at all levels.
Though outsourcing all of the work may not be the only solution for business, increasingly, companies are opting for a hybrid of both AaaS and more traditional methods.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Analytics as a Service: what are the main drawbacks?
While it is good to know the advantages analytics-as-a-service (AaaS) offers, one should also consider the drawbacks.
- Security
A 2020 survey by Statista indicated that more than 83% of respondents identified security as one of the biggest challenges in using cloud services. Make sure to choose a company backed by years of experience and known for high-quality and proven services. While it is enticing to take advantage of those available at a cheap rate, nothing can beat services that are of great value.
- Complexity
The necessity of data analytics can’t be denied, nor does its complexity. The absence of qualified data scientists in a business contributes to unsuccessful projects. If you plan to move to AaaS, internal training for analytics tools is something you cannot afford to overlook. Even if it requires additional costs, having skilled and competent data scientists can provide many opportunities in the future.
- Management
The analytical workflow, in particular, can be demanding. It composes multiple important steps requiring the right people, from data acquisition, modeling, data mining, and visualization. While outsourcing any of these responsibilities may be helpful, do not underestimate the advantage of in-house capabilities.
Conclusion
As we venture ahead, AaaS will adopt advanced analytics, taking more prominence and becoming regular. Equipped with AI and ML techniques, predictive analytics will be commonplace and give rise to foresight instead of mere insights. Businesses of all types will stand to gain massively through AaaS even as they will have a much more powerful and efficient way to shift through, harness, and analyze data.
Imagine the kind of advantage an enterprise will have with a more robust and powerful AaaS solution in hand!
Ascend Analytics provides Analytics-as-a-Service (AaaS), a subscription-based data analytics solution. Ascend offers fully customizable BI solutions with end-to-end capabilities while organizing, analyzing, and visualizing data.