What is Managed Analytics? A Guide to Managed Analytics Services
Managed analytics services bring you the benefits of an expert team and data analytics platform, without the drawbacks of building your own platform and hiring and managing a team. In today’s business world, everyone understands the potential value that data holds for their business.
You know that if you could make use of all the data you have, you would be able to increase the revenue and profitability of your business. But data is scattered among many siloes like internal databases, SaaS products your business uses, and APIs. Bringing data together and making sense of it for meaningful, useful data analytics is difficult.
Managed data analytics services can unlock the value of your data without having to build and manage data infrastructure, and without having to hire and manage a data team.
What is Managed Analytics?
Managed analytics is a service that brings a pre-built data analytics infrastructure, process, and expert team. This can be attractive to companies who want to avoid the cost and risk of building those things on their own and just want to focus on their unique needs around information and answers.
One of Datateer’s customers described themselves as, “we were data-rich but information-poor!” until they worked with managed analytics. Data is central to decision-making for businesses. Unlocking the value inherent in data is challenging. Data is scattered among many siloes in today’s world, so bringing it together is difficult and often a manual, error-laden process. That can be overwhelming for many businesses, especially those that lack the in-house capabilities or time to do so.
Key characteristics of managed data analytics include:
- Consistent, proven processes for data analysis and data engineering
- An inventory of Data Assets
- A fractional, scalable team of experts
- Reporting and controls to allow you to manage the work
- Well defined, scalable pricing
- Systems for:
- Automating data extraction and transformations
- Monitoring the technical health of Data Assets
- Managing and maintaining data quality
Data Asset Management
In addition to building data assets, managed analytics services monitor and maintain these data assets. This is similar to asset management, which is very common in other industries, such as:
- Financial assets. In finance, professional asset managers manage the investment assets of their clients. Clients stay in control, and the asset managers maximize the financial return and maintain safe custody of the assets
- Real estate. Professional asset managers maintain facilities and work to improve and maximize the value of the buildings or other real estate assets they are managing.
- Manufacturing. Manufacturers rely on large, expensive equipment assets to produce their product. Asset managers in this industry monitor, maintain, optimize, and configure these assets for ideal performance.
What are Data Assets?
An asset is anything that brings value to the business. Data is often viewed as intangible, so many businesses do not manage data assets. Managed analytics services treat data as assets, and data assets can be made tangible by identifying, inventorying, and managing the following types:
- Data Sources are databases, APIs, or SaaS products that hold valuable data in silos. They can be Data Sources to managed data analytics, contributing to a centralized store of data available to analyze
- Metrics are defined measurements that bring value to your business. These can be counts, averages, ratios, or other calculations. In managed market analytics, for example, the calculation of customer acquisition cost (CAC) is valuable and can be defined as a discrete Metric.
- Data Products are how people can access information and insights from data analytics. Examples of Data Products are dashboards, reports, and data shares.
Why Managed Data Analytics? What Are the Benefits?
Before investing in managed data analytics, you should consider whether data analytics is right for your company. For most companies, data analytics done right brings strong ROI. Organizations that invest in data analytics grow at 30% annually, compared to all other organizations that average 3% growth (Forrester)! Becoming an insights-driven business promises many clear, measurable benefits.
Informed Decision Making
You and your team make thousands of decisions every day. Some are inconsequential, others are important. Some are routine, and some are new and strategic. Often, data that could offer insights to improved outcomes is locked away in various business systems that your company uses. Bain & Company’s research shows companies with the best analytics capabilities are five times more likely to make decisions much faster than normal.
By bringing all this data together into a central place and making it meaningful, you can have visibility into the reality of your business and customers. By sharing data-driven insights with your employees and customers, everyone can be more informed and make better decisions.
Data is valuable, especially when presented well to the right audiences. Data monetization can come in many forms, but they all require proper cleansing, preparation, and organization to be ready for the intended audience. By providing good data and insights to your existing customers or new customers, you can charge for this value or improve existing services.
Save Time and Effort Getting Answers
Many companies manually pull data from various places and put it into spreadsheets for ad hoc analysis. This time-consuming process is error-prone, requires high effort, and delays getting to answers when they are needed. In contrast, a good data analytics platform makes answers available to you immediately.
Understand Your Customers
Tracking customer interactions with your company can give you a complete picture of who they are and how well you are satisfying their needs. Some of the ways product managers and other use data analytics include:
- Customer 360 is a method of bringing all available data about a customer together in one place. This can be product usage, marketing information, support interactions, feedback surveys, finances, and others.
- Product optimization efforts always benefit from data. By understanding how customers use your product, you can improve the customer experience, product usage, effectiveness, and new feature development
- Customer feedback analysis can identify customers at critical moments, such as those at risk of churning or primed for upselling
Bain & Company’s research shows that companies with the best analytical capabilities are two times more likely to be top financial performers.
Operational Improvements and Speed
Key performance indicators (KPIs) or Metrics are a common way to understand and improve the performance of a business. By making these visible and accessible, people in your business can make improvements to processes. By making these accessible in real time, people can immediately make adjustments and see the outcomes.
What Are Alternatives to Managed Data Analytics?
Managed data analytics are ideal for small and mid-sized businesses, or businesses whose in-house capabilities are overwhelmed by the amount or complexity of data. However, alternatives exist that may be more appropriate for your business.
Alternative 1: Hire a Team and Buy Tools
The classic approach to data analytics is to build your own platform. Many companies do this because of the control it provides or the level of customization they require. However, this approach requires significant investment in salaries and license fees. Here are some things to consider about this approach:
- Will the people with the right expertise be attracted to the opportunity you can provide them?
- Do you have the budget to hire all the breadth of skills necessary?
- Do your infrastructure needs require a custom, in-house solution?
- Do you have the time to build from scratch?
- Will you have consistent work to keep the team working on ROI-producing, meaningful things? (Most organizations see ebbs and flows in the demands for specific analytics work)
See additional information here about the modern data technology stack.
Alternative 2: Do It Yourself
Some companies opt for a strategy of using low-code or no-code solutions designed to make data analytics easy for less experienced people and organizations. This can be beneficial, as it overcomes some of the need to hire a full team, build a full infrastructure, and buy as many tools.
When looking at this approach, here are some things to consider, based on real feedback from clients and others:
- You still need to hire someone to learn and manage the new tool.
- Good data analytics requires domain expertise in your business. This requires people’s time and effort above what a DIY tool can provide.
- These products that aim to simplify the process do so with tradeoffs, including lack of flexibility. When you run into something the tool cannot handle, is it acceptable to abandon that need?
- Some tools are simply an abstraction on top of other tools or open source software already available, with a marked-up price
Alternative 3: Hire Freelancers or Consultants
A proven method for any initiative involving technology is to hire a freelancer or consultant. These may be individuals or firms that provide professional services. This is a great approach because it gives you access to technical and domain expertise that otherwise might be unattainable or too expensive to bring into your team full-time.
Another benefit of outsourcing projects is you can clearly define a scope of work and determine whether the level of investment has sufficient ROI.
When deciding whether to hire external professional services, consider these questions:
- Who will maintain the system once it is delivered?
- Data analytics is often a journey, not a project–will the same people be available in 6 or 12 months for the next phase?
- Does the business model–based on hourly rates or project fees–incentivize behaviors of finding extra work or charging for change orders?
- Many IT companies spread themselves across a wide variety of specialized offerings. Are they specialized in data analytics and your particular needs?
What Are the Pros and Cons of Managed Data Analytics Services?
Managed analytics services can be beneficial to many organizations because they bring many of the benefits of the alternatives while minimizing the drawbacks. To determine whether managed analytics are right for your company, consider these pros and cons.
Pro: Proven Processes and a Pre-Built Platform
Rather than building from scratch, a specialized analytics managed services provider will have a platform where everything is already integrated and works together. They will also have worked out efficient processes that they use for other customers. One benefit of a pre-built platform is a process already set up for monitoring data integrity.
Your company’s specialized needs require custom Metrics and include your unique Data Sources and Data Products. An existing platform and process allow you and the service provider to focus on your unique needs, rather than defining processes and an infrastructure from scratch.
Pro: Cost Effectiveness, Lower TCO
The total cost of ownership of a data analytics solution–which includes people costs, infrastructure costs, and product/tool licensing–can be much lower with managed analytics services than doing analytics in-house.
The most significant factors here are the fractional team, spreading out the costs of products and infrastructure, pre-built infrastructure, and optimized processes.
Working with an outsourced services provider with clear service definitions and methods for interacting can greatly simplify operations. Rather than manage processes and people, you can focus on Data Assets and associated outcomes.
Pro: Agility and Adaptability
A major difficulty introduced by an in-house data analytics platform and full-time team is the inertia they create. Adjusting to new opportunities, or even changing data strategy, can be impossible. In contrast, a managed analytics services provider can be canceled, changed, or refocused based on market conditions or your updated strategy.
Pro: Breadth and Depth of Expertise
Managing and retaining top data analytics talent can be difficult if you can attract that talent to your company in the first place. For this reason, many companies turn to outside help. Data engineering, analysis, and data science are large disciplines with many sub-specialties. A service provider focused on these areas can configure a team that has the breadth of skills and depth of expertise appropriate for your needs.
Pro: Reduced Risk
Companies that build an in-house data infrastructure and team assume the following risks that could be covered by a managed analytics provider:
- High initial investment in building the infrastructure, team, and processes
- Rapid changes in technology and approaches
- Data security and compliance
- Scalability, including size of data as well as workload for people
- Resource allocation
- Upstream changes that break the data pipelines
Con: Timeliness and Availability
Fractional teams may not be as responsive to emergent opportunities and needs. This can partially be addressed by expectations around timeliness of communication and visibility of progress on Data Asset improvements.
Con: Lack of Domain Expertise
The relevance of insights may not be as strong as those coming from domain experts in your company or with years of direct industry experience. This can be mitigated by working with providers with a history of relevant experience, investment in domain-specific solutions, or combined teams where your internal staff has a close working relationship with the managed analytics provider’s staff.
Con: Dependence on Vendor
Any dependence on external vendors introduces risks around service continuity and quality. This is always a tradeoff when deciding whether to do something in-house or work with external providers.
Con: Disguised Freelancer or Consultant
Many traditional consulting or staffing firms are attempting to offer managed services. Unfortunately, their business models conflict with a managed services offering. These organizations are not able to provide the benefits of a true managed services provider.
When Does Managed Analytics Make Sense? Is Managed Analytics Better for Certain Industries?
Managed analytics can work for almost any industry, with a few exceptions. These exceptions have more to do with your data strategy and circumstances than your industry–for example, life science companies that choose to keep all their data out of the cloud and locked away in proprietary systems. But all industries leverage data analytics–marketing, real estate, recruiting, finance, SaaS, even traditionally people-first segments like industry associations and credit unions.
Managed analytics works well for companies that are overwhelmed with data
At Datateer, we often see that product managers, IoT companies, recruiting/staffing organizations, companies developing SaaS products, industry associations, and digital marketers can benefit well from a managed analytics partner. What they have in common is a large amount of data and potential value they could get from it. This can be overwhelming as well as distracting from their core mission.
Managed analytics works well for companies that see data maturity is a journey, not a project
Everyone benefits from an iterative approach to data analytics. Seeing a first version and answering a first round of questions invariably leads to better guidance from stakeholders to guide future iterations. Although a big upfront project can work, it is not the most likely path to success for data analytics maturity.
The second point here is that data needs usually vary over time, with some periods of intense implementation and analysis. At other times, the data assets are being used and providing value, but the demand on people’s time is much less. Companies expecting this variability can benefit from a managed analytics partner who takes on the risk of varying demand.
Managed analytics works well for companies that need to focus on core competencies
Data analysis and data engineering are large disciplines. Many companies struggle to attract and retain talented data people, much less manage them effectively. 70% of data engineers quit every 12 months.
A prime example is a SaaS company with a great application engineering team. Should the company…
- Expect the application engineers to learn data engineering?
- Hire and manage a second team of data engineers?
- Use a managed analytics service to fill the data engineering need?
If the company wants to focus on building depth in core competencies, managed analytics allows them to do so, without giving up on their data strategy.
Managed analytics works well for companies that have to balance budgets
Sometimes organizations receive funding that allows them to build out large teams and infrastructures in anticipation of quickly growing to a much larger company. However, most companies work within budget constraints and must maximize the ROI of each investment.
Managed analytics provides the ability to immediately gain from pre-built data architectures, without having to build from scratch. These services also bring talent in data engineering, analysis, and ongoing operations, sized appropriately for the desired level of investment.
How to Evaluate and Hire a Managed Data Analytics Provider
If you are seriously investigating a managed data analytics provider, making the right selection is important for your success.
The Disguised Consulting Firm
First, confirm whether the candidate is actually providing managed analytics services. The sad reality in our industry is that many staffing or consulting companies promote managed analytics services, but they are really just disguising their staffing or project work.
Although a true managed analytics provider will have some component of professional services, the core part of their offering should be focused on platform and operations.
This means that rather than building your data infrastructure from scratch, they will leverage their core platform for anything that is not unique to your company. Your specific data and metrics are unique to your company–tooling, ticketing systems, monitoring, and data pipeline execution are not. The best balance is when all the non-proprietary concerns are handled by the provider’s platform, while your data is isolated and protected, and your unique needs are customized.
On the other hand, maybe what you really need is a data analytics consulting firm. If so, you'll benefit from our guide How to Hire a Data Analytics Consultant.
Understand the Types of Services Offered
Second, learn the interactions and touchpoints the managed analytics provider supports.
- When can you access them, and how? Do they have a ticketing system to track requests? Live chat support? Dedicated teams of experts?
- How do they handle particularly complicated data? Do they have an escalation path?
- Do they have SLAs (service level agreements), and what do they look like?
- What support do they offer to non-technical people who need to learn and use your Data Assets?
Understand the Pricing Model
Pricing is difficult to get right. For example, early on in Datateer’s history, we tried to keep pricing very simple. This made the buying process easy in some ways. But we realized that we were way overcharging some clients and way undercharging other clients. And our clients felt it as well. This led to too much misalignment.
What you should look for is how their pricing scales and adjusts to your needs. Factors that impact this and will be unique to your company are data volume, data complexity, number of data sources involved, number of stakeholders or audiences, number of subject areas or departments, and minimum levels of data quality.
You should also try to determine whether they are maximizing their margins around hourly consulting rates or projects–a bad sign, indicating they are actually a staffing or consulting company.
You can learn more about general data analytics consultant pricing at How Much Do Data Analytics Services Cost?
Common Concerns with Managed Analytics
"Data Is a Core Competency, We Shouldn’t Outsource It"
Although a company’s data is proprietary–and can provide immense value if used well–the data operations are not. Good data infrastructure and successful data operations look strikingly similar across organizations.
"Outsourcing Is Too Expensive"
Small and mid-sized companies can get all the benefits of a full team and platform for less than the cost of hiring a single full-time employee.
"Outsourcing Is Too Complicated"
Hiring, paying, and managing a team of experts–as well as building out data infrastructure and processes–requires much effort and risk. A well-defined managed services agreement allows the complexity to be handled by the service provider, behind a simple interface of how the companies can interact with each other.
Managed analytics services provide a comprehensive solution for businesses looking to leverage data without the complexities of building and maintaining their own analytics infrastructure. These services offer expert teams, proven processes, and scalable platforms, allowing companies to focus on deriving meaningful insights from their data.
By handling data asset management, informed decision-making, and operational improvements, managed analytics helps businesses realize the full potential of their data, ensuring cost-effectiveness and strategic agility.
This approach is particularly beneficial for organizations seeking to balance budget constraints with the need for advanced data analysis capabilities.