It wasn’t that long ago that when I’d meet with customers my focus would be on educating them on moving to the cloud and easing their anxiety about the prospect of the process. Over the last 18 months, however, the focus has shifted sharply from discovery to execution. For most organizations it’s no longer a matter of if they’re going to move to the cloud, it’s a matter of when.

With that first box ticked, many of these discussions quickly turn to the question of “what’s next?” Customers –– particularly those with multiple datacenters –– want a good understanding of what will change and how they will manage their data when they migrate their on-premises data centers to the cloud. What will they be able to do?

This is when I step in to demystify the Azure Data Platform.

The Azure Data Platform essentially takes the concept of an on-premises data center and moves it to the cloud. Because it’s in the cloud, the Azure Data Platform is more agile, more elastic, and more scalable than traditional infrastructure.

Here are four things that customers considering a move to the Azure Data Platform should consider.

1. Resist the urge to lift and shift.

While some customers immediately hone in on the idea of lifting and shifting their data from their current databases into the cloud, a migration is the perfect opportunity to engineer new design patterns and really leverage the cloud. Organizations that take advantage of cloud design patterns, including cloud management, cost optimization, and new DevOps models for deployments, will not only be able to shed any issues that might currently occur but will be able to architect within the Azure Data Platform in a way that enables innovation.

Tip: More often than not, a greenfield approach is better than the lift and shift approach in the long run.

2. Administering a data center in the cloud is not the same as a data center on-prem.

Their purposes might be similar, but a data center on-prem requires a different skill set than that in the cloud. For even the savviest data center administrators who haven’t learned in the cloud, expect a learning curve. To use an analogy, you might be the best Mercedes mechanic in town, but if someone brings in a Ferrari to fix, you probably don’t have the right parts or tools or even the knowledge to do the job properly. The good news is that this is a surmountable obstacle. I, myself, came from the on-prem world, and during the advent of the cloud was concerned about what this would mean to me as a professional. There are new tools to learn, but as I tell my clients, this isn’t rocket science. You’ve got this.

Tip: Find a partner that will help you remove this friction in a way that works for your business. Our approach at Quisitive is to build the Azure Data Platform immediately, and then work with our clients to start leveraging it. Once they build up their skills and find a level of comfort on the platform, they’re able to take ownership of it. There will always be a transition period, but we’ll be there to help accelerate getting over that hurdle.

3. Find a partner that gets it.

This might seem obvious, but I’ve seen too many cases of organizations that become locked into a partnership because they’re given proprietary solutions. Instead, find a partner that helps you lock into a cloud service provider and that truly understands that provider. At Quisitive, we don’t just recommend Microsoft products. We’re a Microsoft partner, which means that we are able to lean on Microsoft for providing guidance and helping our customers move forward within the Azure Data Platform. Because of this close connection we’re able to take our customers from dark to cloud in a concise way.

Tip: Understand the framework you’re working within before choosing a partner. We’ve developed the On-Ramp to Azure Data, which provides clarity on processes, benefits, and deliverables.

4. Resist the urge to look back.

Moving your organization’s data to the cloud from an on-prem data center is a daunting task. We get it. It’s why for many organizations, we move assets over to the cloud gradually, taking a trickle approach. Along this process, customers reach the point where they feel comfortable enough to go full speed ahead. But sometimes, organizations running their entire business on perm are skeptical about the benefits they’ll find once they’re operating in the cloud and require a full replication of their on-prem environment in case they decide to switch back. This approach will dramatically drive up costs and complexity.

Tip: Take an iterative approach and find a level of comfort before taking the next step. Even knowing that there may be bumps along the way, the likelihood of reverting back to an on-prem data center is minimal once you’ve experienced the agility, flexibility, and scalability of the cloud environment.

Is your organization ready to make the move? Let us know how we can help.

It’s no secret. Azure Data & Analytics can be an incredible conduit for realizing business value, implementing powerful new solutions within the cloud, and gaining access to vast amounts of valuable data that is generated every day through your organization’s business activities. With the power of Azure Data & Analytics on your side, you can implement new business applications that can: stream and interpret massive blocks of data, generate powerful reports for better decision-making, see the impact of decisions in real-time, and much more. 

There has been a fundamental shift in the way successful businesses scale and process their data which is, in many cases, one of their most valuable assets. Designing and implementing a platform that can capture—and leverage this data in meaningful ways, can be a daunting yet extremely rewarding task, but is a crucial step in a successful digital transformation strategy. 

Here are some common questions your team may ask when brainstorming an implementation of Azure Data & Analytics for your organization: 

• How do key stakeholders within the organization work with IT to capture and define business use cases and user stories for the technical platform?
• How should the governance and foundation work of our new data platform be established? Who will be responsible for maintaining this platform and ensuring security and best practices in its day-to-day use?
• How do we expand and build on crucial applications to support capturing and utilizing more business data?
• Within the new platform, what forms of automation can we implement to decrease costs, improve efficiency, and augment existing capabilities?
• What out-of-the-box tools are available to us to expand our capabilities with minimal investment?
• How do we accurately predict and control costs associated with the new business platform and ensure the integrity of our governance is maintained?
• Who will be responsible for ensuring our business applications are maintained within the new platform?

The list of crucial questions in this stage can seem endless and exhaustive, but it only highlights the need for careful and deliberate decision making when implementing a new technological platform of this type regardless of the planned scope. Regulation compliance, security best-practices, and migration framework contribute to any successful cloud strategy, whether it’s designed for a small business or a large corporation. Whether you decide to work with a trusted and experienced partner or take on the challenge internally with your team, asking and answering the right questions and being thorough in all steps of planning and implementation is crucial to maximizing your chance of success from the start. 

Building your Azure Data & Analytics platform 

Without proper knowledge or experience, planning and building your organization’s Data & Analytics platform in Azure without professional aid can take months or even years. Dragging timelines, uncertainty in costs, and other factors can delay implementation and reduce confidence in a successful cloud migration plan when there is limited or non-existent experience to lean on. 

At Quisitive, we have been working as a trusted partner to help migrate customers of all sizes to Azure and work with them to realize immediate business value from their investments in the cloud. 

Whether you are working with a trusted partner or pursuing the cloud platform build or migration alone, the importance of cloud adoption framework cannot be overstated. This framework is a living artifact and represents a reliable source of truth in your cloud strategy. It is a “true north” when considering new opportunities in the cloud and understanding how your organization’s overall IT strategy is adapting. Bolting it on after the build or migration rather than baking it into a cloud strategy from the start will reduce your chances of success.  

Just because the cloud adoption framework is defined from the start doesn’t mean it can’t evolve over time to include more stringent and defined criteria as your organization matures and expands in its cloud strategy. 

Extensive and carefully planned migration framework ensures your organization has put proper consideration into the numerous challenges and obstacles of a successful cloud migration strategy and demonstrates a fundamental understanding of how the platform will be used to solve business challenges and achieve new goals as they emerge. This work, at least on a foundational level, needs to be done before any VMs are spun up, or any data is migrated to Azure. 

Getting into the actual build and deployment of the platform, your organization needs to be able to estimate the right-sized resources your business will need within Azure. Allocating too many resources across Compute, Storage, and other areas of Azure can drive up costs and cause your tenant to burn too hot, diminishing the business value that could be achieved from a properly sized and implemented data and analytics environment. Allocate too few resources and your platform may slow, drop tasks, and place a constraint on associated business processes. 

Constant adjustments, automation tweaks, and refinement of cloud strategy can ensure ongoing improvement and optimization within the cloud. The result? A *reduction* of uncertainty, unpredictable cost, and unoptimized resource utilization. 

Working with a trusted partner to perform a cloud assessment can be an incredible first step to ensure ongoing improvement and optimization within the cloud from the onset.  

Know your options, and unite them with your business use cases and user stories 

Within Azure, there are several out-of-the-box Data and Analytics products that can be added to your existing or newly acquired subscription with little to no additional cost. Some of these tools and applications can be custom-tailored to your organization’s needs, and others give immediate access to new capabilities including how your business is able to use and interpret data from different sources. 

You can also build your own custom solutions to address your business problems head-on, using some of these available products and services as building blocks or framework to guide your efforts. Self-guided continuous improvement and exploration in the cloud, while *costly*, can lead to refined strategies and capabilities within Azure—allowing your team to grow their understanding of Azure over time. 

Whichever route you pursue, it is important to tie the technology to the fundamental business use cases and user stories which it is trying to solve. 

• How can this solution help key stakeholders make better decisions and respond to market data on a more regular basis?
• As a manager, how will this platform help me better track the performance of my team against quarterly business goals and planned product enhancement deployments?

Always look to understand the ‘why’ behind any technological solution and understand the ties to crucial business use cases and user stories that drive success to the organization. In this case, business and technology can go hand-in-hand, and when used and evaluated together, can have powerful results. This is the premise of the Azure for Business blog series: to help you understand new ways of considering how business and technology can interact to create exciting new synergies that yield incredible results. 

In conclusion, planning an Azure Data & Analytics implementation for your organization can be a daunting task, but there are numerous steps that can be taken to better ensure success from the start. Whether your organization is taking on the migration and implementation singlehandedly or working with a trusted partner, make sure to include proper consideration for cloud migration framework, security and compliance, and well-defined governance. 

Fully Functional Azure Data & Analytics for your organization in just 30 days

Looking for a trusted partner to guide you in your implementation or just looking to learn more about the potential business use cases of Azure Data & Analytics?  

Lean on Quisitive’s expertise for your implementation: our On-Ramp to Azure Data & Analytics program gives organizations a fully functional, custom-tailored Azure Data & Analytics platform in just *30 days.* Microsoft will fund a percentage of the program cost to help you realize immediate business value from your investment. Learn more about the program and see if your organization is eligible for Microsoft funding right here. 

At Quisitive, we subscribe to the philosophy that data is an essential asset of any business. Unfortunately, data sometimes gets a bad rap, which is perhaps understandable. Many businesses become reliant on dated technologies to store and organize expanses of historical data. They also struggle to manage and consolidate multiple data sources and often green-light indiscriminate data projects that, instead of reaping insights, simply leave teams and leaders frustrated.

These data challenges are not uncommon. One of our recommendations centered on the notion that leaders must focus their efforts on the questions with the greatest potential to impact the business positively. While this may seem intuitive, focusing on these questions warrants further expansion on business goals that drive value from data. However, we’ve found that many business leaders couldn’t articulate what exactly they were hoping to accomplish once they made the move to an as-a-Service cloud-based model.

In a basic sense, leaders are typically seeking a single view of their business or desire a single view of their customers (what is often referred to as a 360-degree view of the customer). This, however, is insufficient in understanding the actions that will be taken to accomplish core business objectives. Through our experience in working with clients over the past 16 years, we have found that most data projects are launched to achieve one of seven business goals. By identifying business objectives at the onset of the project, we can more clearly estimate the opportunity, define success, and develop a comprehensive data strategy and analytics roadmap. Thus, the seven business goals for deriving value from data are described below.

1. Improve Customer Acquisition

This customer-centric goal allows organizations to develop a more targeted marketing spend. Business leaders hope to increase gains by either improving conversion rates or increasing revenue with reduced marketing spend. Rather than using a “blanket approach” or a “one size fits all” method to marketing and customer messaging, companies are seeking to use their data to better target and segment their customer base, predict who is most likely to convert, target those with the highest Customer Lifetime Value, and personalize promotions through timely and relevant messaging. Therefore, if you are looking to improve customer acquisition, data should be used for:

• Customer Segmentation: Grouping customers together by similar profiles or behaviors
• Lead Scoring: Evaluating target customers’ chances for conversion
• Customer Lifetime Value: Predicting total anticipated revenue generated from each customer throughout the customer journey
• Personalization: Targeting prospects with content/offers that increase chances of conversion
• Funnel Analyses: Mapping the customer’s buying journey to determine what motivated the sale or uncover where the sale was lost

2. Generate More Revenue from Existing Customers

A second customer-centric objective is to increase revenue from your existing customer base by expanding exposure to other product offerings. Once you get them, how do you get more money from them?  This typically involves some level of personalization, such as recommending specific products for individual customers based on past purchase history or current content-browsing. Recommended product algorithms are the cornerstone for upselling, down-selling, and cross-selling, keeping in mind that there are times when down-selling is more profitable than upselling or cross-selling (e.g., generic brands, used products, or any other situation where less expensive products/services have a lower cost of acquisition/implementation. Ultimately, this business goal is about using data to identify customer characteristics and behaviors to better anticipate their needs. Therefore, if you are searching for ways to generate more revenue from your existing customer base, data should be used for:

• Personalization: Enhanced recommendations based on knowledge of a customer’s purchasing/browsing behaviors
• Upselling, Down-selling and Cross-Selling: Most often accomplished via recommended product/service algorithms (either through “real-time” offers or batch follow up campaigns),
• A/B Testing: Experimental testing of marketing and sales activities and/or design choices
• Pricing Analysis: Predicting pricing sensitivities for dynamic pricing models

3. Retain Customers

A third customer-related goal anticipates losses to a customer base by providing evidence of who is likely to leave, what the common causes are, and how a company should best intervene. Intervention strategies, such as personalized messaging linking the potential cause to a likely future behavior, promote a better experience, and encourage customer retention. Focusing on this goal permits companies to provide the right resources to the right customer. Keep in mind that some customers are not worth saving; that is, they can be a resource drain which ultimately costs you more to maintain than their Customer Lifetime Value would dictate (though we must be careful how we drive customers away in a world of expansive social media reach).  If you are trying to stop the bleeding, data should be used for:

• Customer Churn: Predicting when customers will leave before they leave
• Personalization: Connecting customer segmentation and agent data to target the customer service experience and personalize the intervention
• Customer Lifetime Value: Understanding which customers are worth saving based on their anticipated total lifetime value
• Social Media Analytics: Using text analysis to understand brand awareness, reveal customer sentiments, and enhance customer engagement

4. Reduce Operating Costs

Reducing operating costs should be a goal of every company to increase the bottom line and ensure that both effort and funding are expended appropriately. Companies that operate in the status quo without analyzing data for potential efficiencies and optimizations are unable to grow. Eventually, competitors will catch up and find ways of offering products and services faster, with lower pricing. Data is critical for evaluating where the company is hemorrhaging undue resources and generating a plan for optimizing production potential—including workforce, machinery, and logistics. Data can identify patterns of costly operating procedures and outcomes—such as customer fraud, inefficient distribution, or employee turnover—that cost the business money. If you are evaluating ways to reduce operating costs, data should be used for:

• Analytics Dashboards: Providing “real-time” insights into operations and costs by creating a single view of the business by incorporating multiple sources of operational and financial data
• Voluntary Employee Turnover: Predicting which employees will leave before they leave
• Workforce Management: Optimizing level of experience and team structure for maximum return on investment
• Preventive Maintenance: Using sensor data to anticipate equipment failure
• Production Optimization: Maximizing production while balancing operating costs
• Supply Chain Optimization: Recommending logistics based on anticipated demand and potential downstream bottlenecks
• Fraud Detection: Identifying anomalous patterns of behavior as indicators of costly fraudulent activity

5. Calculate Risk

A fifth goal of organizations involves assessing risk. Risk assessments are useful when companies need to prescribe actions based on calculated risk and reward scenarios. Critical for planning and increased predictability, sophisticated algorithms dictate when an action should be chosen over another, and what stands to be gained or lost as a result of that decision. Data science techniques centered on root-cause analysis are especially helpful in understanding the types of failures, frequency of failures, effects of failures, and potential severity of effects. Some common applications where data is leveraged to assess risk include:

• Actuarial Science: Identifying predominant risk factors and calculating associated risks, especially common in insurance, loans, and finance recommendations
• Litigation Risk: Assessing likelihood of a costly lawsuit associated with company’s operational procedures
• Health Risk: Potential complications associated with human behaviors or health procedures
• Go/No Go: Any prescriptive operational decision made by risk assessment

6. Improve Forecasting

The sixth business goal focuses on predicting future outcomes for better planning. This often includes having a clear understanding of which products or services are in demand and how a company can be ready to capitalize on that demand when the opportunity surfaces. Improved forecasting implies that companies are no longer leaving money on the table because they weren’t properly prepared, such as having the appropriate inventory in the preferred location at the relevant time. Ultimately, increased visibility into upcoming sales cycles equals better planning for a company’s future. To improve business forecasting, data should be used for:

• Sales Forecasting: Predicting sales for any period of time
• Demand Forecasting: Predicting demand for a product or service over a designated time period
• Inventory Management: Adjusting inventory levels based on anticipated demand and other market factors
• Pricing Analysis: Dynamically adjusting pricing based on anticipated availability of a product or service to maximize profitability

7. Create an Entirely New Revenue Stream

Finally, an organization may want to capitalize on its data by developing an entirely new revenue stream. This is especially common in B2B companies where businesses can develop products or offer services centered on creating insights for their customers. That being said, more and more B2C companies are realizing the potential of their data for deriving insights that customers are willing to pay a pay a premium for (e.g., premium memberships/subscriptions providing access to customers’ own usage data). Every business should look for opportunities to create new revenue streams and potentially invent a new market for which there is no competition. To best accomplish this goal, companies should consider:

• Product Development: Developing enterprise-level analytics products
• New Service Offerings: Capitalizing on data to provide additional services back to customers
• Application Creation: Creating apps to automate processes and facilitate data collection and analysis

Today, business leaders are using data to acquire more customers, generate more money from their existing customers, retain customers, reduce operating costs, calculate risk, improve forecasting, and create an entirely new revenue stream. When the proper business goals are aligned with a solid data strategy and the proper technologies, business leaders quickly learn that data can drive true transformation, which can, in turn, amount to significant revenue generation and cost savings.