Tag: analytics

DXP Series, Part IV: DXP and Data-Driven Decision-Making

Posted by on December 06, 2017

Business team meeting analysis financial chart together at cafe.

Think about how you make important business decisions. Decision-making begins at the point where intuition takes over from analyzing the data.  If your data analysis carries far less weight than intuition, your decision process may not be taking full advantage of available data.

If so, you are not alone. Bi-Survey.com surveyed over 720 businesses. The survey found that 58 percent of respondents based about half of their regular business decisions “on gut feel or experience.” On the other hand, over 67 percent of those businesses “highly valued” information for decision-making, and 61 percent considered information “as an asset.”

The survey showed that when businesses were not using information as the basis for decision-making, it was because the information was not available or reliable. They were either not collecting it or were not using what they had.

KPIs are there, but not the data to read them

Another significant finding involved the role of key performance indicators. There is an important connection between KPIs and the data that measure and drive them. Here is where another disconnect stood out like a beacon: Nearly 80 percent of the companies had defined and standard sets of KPIs, but only 36 percent were using them “pervasively across the organization.”

So there was an obvious disconnect between valuing the information and a willingness to use it. In this post we shall address that contradiction and explore ways to close the gap between valuing the data and using it for data-driven decisions.

How DXP leverages data analytics

The road to data-driven decisions must go through data analytics.  In a previous blog, we discussed how data analytics and other tools plug into the realm of DXP. Data analytics are what help you find meaning in the data you generate and collect.

Those meanings are what drive the decisions and strategies that focus on efficiency and excellent customer service. In terms of business decisions, the ones based on verifiable and quality data are the most beneficial to the business. They are data-driven.

So, data-driven decision management is a way to gain advantage over competitors. One MIT study found that companies who stressed data-based decisions achieved productivity and profit increases of 4% and 6%, respectively.

Two “how-tos” to get on the road to data-driven decision management

#1. How to head towards a data-driven business culture (and benefit from it)

The survey showed that respondents were operating at half capacity when it came to using data-driven decision methods. To unlock the process as well as the data, businesses need to do the following:

  • Focus on and improve data quality.
  • Ease and lower the cost of information access. Break down those proprietary silos and use the best data-extraction tools available.
  • Improve the way the organization presents its information. There are many outstanding presentation products on the market.
  • Make the information easier to find, and speed up the process where users can access the information.
  • Get senior management on board and aware of the value of business intelligence and data-based decision making. Promote a culture of collaborative decision-making.

#2. How to improve internal data management

Data governance (where the data comes from, who collects and controls it) is a major obstacle to taking advantage of data-driven decision benefits. Survey recommendations were that companies should take the following steps:

1. Build an IT architecture that is agile and which can integrate the growing number of data sources required for decision-making. Plug into external big-data sources and start harvesting them.

2. Look for ways to break down barriers to promote cross-departmental cooperation and data alignment. A business intelligence competency center (BICC) can play a major role in achieving that goal.

3. Re-define and use KPIs across the organization and align those measures of success with a focus on data governance.

A strategy for applying data-based decision-making

Bernard Marr in his Forbes online piece, provides the following suggestions for any business to for applying data to decision making:

1. Start simply.

To overcome the overload of big data and its endless possibilities, design a simplified strategy. Cut to what your business is looking to achieve.  Rather than starting with the data you need, start with what your business goals are.

2. Focus on the important.

Concentrate on the business areas that are most important to achieving the foregoing strategy. “For most businesses,” says Marr, “the customer, finance, and operations areas are key ones to look at.”

3. Identify the unanswered questions.

Determine which questions you need to answer to achieve the above focus. Marr points out that when you move from “collect everything just in case” to “collect and measure x and y to answer question z,” you can massively reduce your cost and stress levels.

4. Zero in on the data that is best for you.

Find the ideal data for you: the data that will answer the most important questions and fulfill your strategic objectives. Marr stresses that no type of data is more valuable or inherently better than any other type.

5. Take a look at the data you already have.

Your internal data is everything your business currently has or can access. You are probably sitting on much of the information you know you need. If the data has not been collected, put a data collection system in place or go for external resources.

6. Make sure the costs and effort are justified.

Marr suggests treating data like any other business investment. To justify the cost and effort, you need to demonstrate that the data has value to your long-term business strategy. It is crucial to focus only on the data you need. If the costs outweigh the benefits, look for alternative data sources.

7. Set up the processes and put the people in place to gather and collect the data.

You may be subscribing to or buying access to a data set that is ready to analyze, in which case your data collection efforts are easier. However, most data projects require some data collection to get them moving.

8. Analyze the data to get meaningful and useful business insights.

To extract those insights, you need to plug into the analytics platforms that show you something new. Look for platforms that squeeze out the reports, analysis, and switchboard displays that tell you what you need to know.

9. Show your insights to the right people at the right time.

Do your data presentation in a way that overcomes the size and sophistication of the data set. The insights you present must inform decision-making and improve business performance. Go for style, and substance will take care of itself.

10. Incorporate what you learned from the data into the business.

Here is where you turn data into action. When you apply the insights to decision making, you transform your business for the better. That is the crux if data-driven decision-making. It is also the most rewarding part of the venture.

Summary and Conclusions

1. Business decision-making based on data results in greater reliability, efficiency, and profitability. DXP leverages data analytics towards the goal of more data-based decision making and achieving a competitive advantage.

2. Migrating towards a data-driven business culture requires unlocking the 50 percent of the decision-making and data currently not being used. It requires improved internal data management and governance and breaking down barriers to internal communication.

3. Finally, when those barriers are down, you can begin a strategy for applying data-based decision making. Start simple and focus on what business areas you need to improve and determine what data you need. No data is better or more valuable than any other; the key is to find the data that meets your objective, analyze it, and translate it into actionable decisions and improvement.

DXP Series, Part II: DXP and the Customer Experience

Posted by on November 28, 2017

Multi-ethnic young people using smartphone and tablet computers

Introduction

In Part I of our DXP Series, Is a Digital Experience Platform Right for My business, we highlighted how a digital experience platform (DXP) is a set of tools to manage the customer’s online experience.  According to Liferay, the obsession with customer experience is at the confluence of the following factors:

  • Customers interact with companies on a wide variety of digital channels (web, mobile, social media)
  • Customers demand and expect the same experiences they get from digital leaders like Google, Apple, and Facebook.
  • Social media has become the cheer- (and jeer-) leader as an unstructured way to talk as customers provide feedback and influence public sentiment.
  • Mobile devices are on the scene and immediate. They give companies additional ways to stay in touch with customers.
  • The ability to get deep customer insights provides targeting information for a single person and give that person an highly personalized experience. Those insights come through everything from analytics to scooping up big data on social media.
  • Digital technology evens the playing field. Startups can disrupt traditional industries. Think: Uber and Airbnd. Those upstarts can deliver a better customer experience. Those startups have easy access to a tool kit that becomes a platform, not just dispersed ad hoc applications.

9 pieces in the DXP toolkit

The DXP toolkit can be a platform based on a software bundle, suite, or a single piece of software.  We listed the most common platforms as follows:

  1. Content management—allowing non-technical users to fill and maintain your DXP
  2. Social media—going into the wilds and deeper into the user realm
  3. Mobile website integration—fitting your DXP to the small screens viewed by millions
  4. Portal or gateway—passage and security without the latter inhibiting the former
  5. Search functionality—finding what users are looking for so they will stick around
  6. Rich Internet Application tools (RIA)—enriching the user experience through motion and interactivity
  7. Collaboration and meetings—working together face-to-face where many heads are better than one
  8. Analytics—getting feedback and breaching the gateway to AI
  9. Backend management—maintaining the DXP behind the scenes

In this post we describe those platforms and explore ways in which DXP integrates its technologies, components, processes, data, and people. That integration explains why DXP is keeping up with the push for customer and employee engagement.

1. Content is king, but users must rule

A content management system (CMS) is an application or set of related applications used to create and manage digital content. Think of CMS as a kind of digital word processor or publisher that dumps content into your website. It is more than that, of course.

CMS makes it simpler for content creators—the people who really know the business–to manage a website without developer assistance. In larger enterprises with multiple users adding content on a regular basis, a CMS is the easiest way to keep the site content up-to-date and responsive to search engines. Your platform is only as good as its content and you need a user-friendly way to keep content current.

2. Social Media is a vast channel for exploitation

Plug in social media to a DXP and open your web portal to the data- (and customer-) rich world of Facebook, Twitter, LinkedIn, etc. Social media plays a vital part in any migration to DXP. Users want the option to configure their own social media sharing. In addition to the out-of-the-box social media capabilities that often come with modern DXP platforms, there are also a variety of plug-ins available that can further extend and customize these capabilities.

3. Mobile channels reach millions

Your DXP product may look good on the desktop computer screen, but without a mobile platform you are missing out on millions of mobile users. Mobile phones have migrated from voice communication devices to ubiquitous pocket computers. According to Statista, by 2020 there will be nearly 258 million smartphones in the U.S.

Add a mobile version of your DXP platform to fit the small screen and expand your marketing base exponentially. Again, customers expect their mobile applications to be as excellent and friendly as the hardware they use.

4. Portal or gateway access gives users a passport and the warm feeling of security

Web portal software can create the following interface points for DXP:

  • Customer portals to create transactions and access documents and information online
  • Partner/agent portals to help field agents, partners, and franchises become more effective by accessing proprietary and personalized information
  • Business process portals to access and track complex business processes

A cloud-based DXP needs a web portal that both locks out hackers and performs the handshaking crypto-rituals to open the locks.

5. Search functionality adds power to DXP web pages and applications

Pathways to built-in search functions handling customer queries are tools to win the race between customer engagement and the impatience of today’s users. Adding a search application to a DXP portal allows drill down. The drill down must go through content types, tags, as well as categories the user specifies to refine the search. The search application can be placed on a page or be a link to allow users to do a web page content search.

6. Rich Internet Application Tools supercharge DXP

RIAs are web applications having similar characteristics of desktop application software. They add functionality to DXP with tools like Adobe Flash, Java and Microsoft Silverlight. As the name implies, these tools provide a “richer” experience for DXP. RIAs provide movement, user interactivity, and more natural experiences for everyone accessing the DXP. Add a sense of time, motion, and interaction to a DXP, and the users will stick around and enjoy the experience.

7. Collaboration and meetings make the enterprise go ‘round

Collaboration suites can resonate with other DXP apps to promote excellence in communication. They are message boards for team discussions, blog platforms and meeting software to add to your inventory of rich content.

Use document management to collaborate, brainstorm, and produce quality content.  Plug in meeting software for worldwide, real-time worldwide, face-to-meetings and conferences with real colleagues and customers at a fraction of travel costs.

8. Analytics software provides an eagle-eyed view

The best way to improve user experience is to know who, how many, and the characteristics of those who visit your DXP.  You want to know how your site or service is performing, who is back-linking to you, and to be able to dig into gathered statistics for visitor regions.

Analytics also provides a dashboard view to do the business intelligence magic of process measurement and customer behavior. A DXP partnered with analytics is the foundation for moving to artificial intelligence.

9. Backend management is your behind-the-scenes DXP management tool

Backend technologies help you manage your DXP or web application, site server, and an associated database. Backend developers need to understand programming languages and databases. They also need to understand server architecture.

On the other hand, as they say, “There’s an app for that.” DXP users can access backend manager technology in the cloud through MBaaS offerings. 

Conclusion: DXP is not just an eclectic collection of software

The platforms described above can work together to solve the biggest challenge enterprises face in the digital age: customer obsession. Companies are undergoing digital transformation in every area from business process to customer analytics. DXPs can bring all that together and re-engineer business practices to be totally customer oriented.

Digital transformation is the challenge. DXP is the solution.

How Digital Experience Management Differs from Content Management

Posted by on October 12, 2017

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When considering Digital Experience Management and Content Management, it’s best to have a concrete definition of both terms to fully understand how they differ.

What is digital experience?

Digital experience includes a number of things, including communications, processes and products from every digital aspect that engages an audience. This includes wearables, use of the web and mobile devices, beacons and recognition. The information gathered is analyzed to provide insight into customer relationships, identity, and intentions as they interact with businesses and organizations. This helps determine how companies deliver these digital experiences for their customers for future success.

What is content management?

Content management is also known as CM or CMS. It involves the collection, acquisition, editing, tracking, access and delivery of both structured and unstructured digital information. This content includes business records, financial data, customer service data, images, video, marketing information and other digital information.

With content management, you create and manage content, finding ways to generate awareness across multiple channels to reach more people. While having content is good, it’s more about offering the entire “experience” to the user that will give them more enhanced, enriched engagement. Content Management Systems have continuously evolved, integrating contextual digital experiences. This requires a comprehensive and effective strategy, the right tools, the right approach, and most of all, the right technology. An optimal digital experience embraces all these elements to provide personalized, responsive, and consistent experiences for every user you engage.

How is this done?

Digital experience (DX) management works in conjunction with content management, but is more comprehensive and fulfilling to the user. Think about the different outlets that engage customers – websites, social media, microsites, text messages, mobile apps and more. All these elements offer a complete digital experience. The processes and technology that provide these customized, consistent experiences is the management of it all.

One of the best ways they differ is that in digital experience management, the distribution channels all have objectives to follow and limitations. These help drive specific requirements for content and how to manage it. For instance, your tone and CTA will be different based on the digital platform you use. Additionally, when interacting with content, users want personalized experiences based on analytics you have determined appropriate for that channel. This allows them to seamlessly interact within that experience.

Web publishing used to be the first line of engagement, but not anymore There are too many channels users interact with that require ways in which publishers can gather feedback to quickly adjust their content. Without this management model in place, the system will not work.

Tools of the trade

There are a number of tools and systems to manage the digital experience. There are options for advanced analytics, to enhanced marketing tools that manage content based on channel. There’s also an emerging breed of Digital Experience Platforms (DXP), which provide businesses with an architecture for delivering consistent and connected customer experiences across channels, while gathering valuable insight and digitizing business operations.

When you have systems that work well together, being able to track successes becomes easier. When determining which tools will work best, you may want to start with product mapping. As a basis, the digital experience tool should include a combination of inbound marketing automation, analytics, and content management. Getting a system developed to meet all your needs is key.

As different avenues of engagement now drive the customer experience rather than the web, delivering a comprehensive and holistic experience is key. The digital experience is more complete, diverse and authentic – future thinking, while integrating content is how it should be done.

Optimizing Your Customer Experience Management

Posted by on August 15, 2017

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A customer’s experience with your organization may, in fact, be more important than the quality of either your products or your services. Customers today want to feel valued — they want to be able to have their needs both anticipated and fulfilled. Improving upon and optimizing your customer’s experiences is called customer experience management. Through new technologies, there are many ways that you can improve upon your customer experience management and, additionally, your ROI.

Integrate Your CRM, Marketing Automation, and Media Solutions Into a Single Infrastructure

Optimizing customer experience begins with consolidating and analyzing your data. To that end, integrating your CRM and marketing solutions can be an incredibly effective first step. Comprehensive CRM and marketing automation solutions — such as Salesforce, Marketo and HubSpot — almost universally come with third-party integrations out-of-the-box. For more distinct infrastructures, APIs, importing and exporting, or custom programming may be required. Regardless, this will create a single infrastructure that contains all of your customer information.

Not only does this improve analytics, but it also improves customer care overall. Both customer service representatives and sales personnel will have all of the information they need to quickly service the customers and get them the information that they need. Marketing campaigns will be able to target customers based on their prior behaviors — and will be able to prompt them towards purchasing more effectively.

Develop an Omni-Channel Approach through Content Management Systems

Content Management Systems (CMS) make it easier to push content directly to a multitude of different channels. Social media, email marketing, and websites can all be consolidated under a single content system — so that a single push of the button can update customers on a variety of platforms. Omni-channel approaches make it easier to scale your organization upwards and to reach out to individuals across multiple demographics and interests. Through regular content distribution, companies can achieve better organic growth and improve upon their inbound marketing.

A CMS is particularly useful for lead procurement and demand generation. With the use of a CMS, a strong and strategic digital marketing campaign can ensure that leads come to the business rather than the business having to procure leads. Organizations are thus able to improve upon their ROI, extend their marketing reach, and refocus their budget to additional areas of advertising and support.

Explore Big Data, Such as Emotional Analytics and Predictive Intelligence

Emotional analytics and big data can work together to develop new strategies for customer acquisition and retention. Algorithms are now available that are substantially advanced that they can look at patterns of customer behavior and determine the best way to service that customer. At its most complex, emotional analytics can involve motion capture and facial analysis, in order to detect micro-expressions that may aid in detecting the customer’s emotional state. But this isn’t the type of analytics that would most commonly be used by a business. Businesses, instead, would most likely use text-based analysis or verbal analysis, to detect the best leads based on their word usage and the amount of emotive statements they have made.

Not all big data is so complex. Predictive intelligence can also be much simpler, such as looking at a customer’s past purchases and predicting when they will need to make further purchases. Predictive intelligence is used to fantastic effect on many e-commerce marketplaces, to suggest items that may be relevant to the consumer based on the items that they have either purchased or browsed. Predictive intelligence can also be used to detect and identify certain patterns, such as whether a customer may have abandoned a shopping cart due to high shipping charges.

Create Knowledge Management Systems for Superior Customer Service

Customers today often prefer to self-service. A solid customer service experience is, thus, often one in which the customer does not need to contact the organization at all. New help desk and support solutions can be nearly entirely automated, so that customers can get the answers they need out of a knowledge management system. This management system may take the form of a helper website or even a live chat with a bot. When self-service fails, customers prefer a variety of ways to communicate: through email, phone, instant messaging, or even text message.

By providing these additional resources for customers, organizations not only assist the customer in getting what they want, but also reduce their own administrative overhead. The more customer service can be automated, the less time and money the organization has to sink into technical support and customer service personnel.

It’s an exciting time for organizations looking to improve upon their customer experience. Through better customer experience management, companies can fine-tune their operations and ensure that their customers keep coming back.

Find Meaning in Your Data With Elasticsearch

Posted by on March 28, 2017

We’re surrounded by data everywhere we go, and the amount is growing with each action we take. We rely on it regularly, probably a lot more than we even realize or would like to admit. From searching for nearby restaurants while traveling, to reading online product reviews prior to making a purchase, and finding the best route home based on real-time traffic patterns, data helps us make informed decisions every day.

However, all that data on its own is just data. The real value comes when you can find the right, relevant data when it’s needed. Better yet, take it a step further and find meaning in your data, and that’s where the real goldmine is.

Businesses are increasingly turning to search and analytics solutions to derive more value from their data, helping to provide the deep insights necessary to make better business decisions. Some popular use cases include:

  • Intelligent Search Experiences to discover and deliver relevant content
  • Security Analytics to better understand your infrastructure’s security
  • Data Visualization to present your data in a meaningful way
  • Log Analytics to gain deeper operational insight

At Rivet Logic, we realize the importance of data, and see the challenges businesses are facing in trying to make sense of their growing data pools. We’re excited to have partnered with Elastic – the company behind a suite of popular open source projects including ElasticsearchKibanaBeats, and Logstash – to deliver intelligent search and analytics solutions to help our customers get the most value out of their data, allowing them to make actionable improvements to websites for enhanced customer experiences!

A Real-world Use Case

How might this apply in a real-world scenario, you ask?

An example is a global hospitality customer of ours, who has partnered with Rivet Logic to implement three internal facing web properties that enable the company to perform its day to day business operations. With a reach spanning across 110+ countries, these sites are deployed in the cloud on Amazon AWS throughout the US, Europe and Asia Pacific, consisting of many data sources and used across multiple devices.

This customer needed a way to gain deeper insight into these systems — how the sites are being used along with the types of issues encountered to help improve operational efficiencies. Using Elasticsearch and Kibana, this customer is able to gain much better visibility into each site’s utility. Through detailed metrics, combined with the ability to perform aggregations and more intelligent queries, this customer can now gain much deeper insight into their data set through in depth reports and dashboards. In addition, the Elastic Stack solution aggregates all system logs into one place, making it possible to perform complex analysis to provide insightful data to better address operational concerns.

 

NoSQL Design Considerations and Lessons Learned

Posted by on July 29, 2015

At Rivet Logic, we’ve always been big believers and adopters of NoSQL database technologies such as MongoDB. Now, leading organizations worldwide are using these technologies to create data-driven solutions to help them gain valuable insight into their business and customers. However, selecting a new technology can turn into an over engineered process of check boxes and tradeoffs. In a recent webinar, we shared our experiences, thought processes and lessons learned building apps on NoSQL databases.

The Database Debate

The database debate is never ending, where each type of database has its own pros and cons. Amongst the multitude of databases, some of the top technologies we’ve seen out in the marketing include:

  1. MongoDB – Document database
  2. Neo4j – Graph based relationship
  3. Riak – Key value data store
  4. Cassandra – Wide column database

Thinking Non-Relational

When it comes to NoSQL databases, it’s important to think non-relational. With NoSQL databases, there’s no SQL query language or joins. It also doesn’t serve as a drop-in replacement for Relational Databases, as they are two completely different approaches to storing and accessing data.

Another key component to consider is normalized vs. denormalized data. Whereas data is normalized in relational databases, it’s not a necessity or important design consideration for NoSQL databases. In addition, you can’t use the same tools, although that’s improving and technology companies are heavily investing in making their tools integrate with various database technologies. Lastly, you need to understand your data access patterns, and what it looks like from the application level down to the DB.

Expectations

Also keep in mind your expectations and make sure they’re realistic. Whereas the Relational model is over 30 years old, the NoSQL model is much younger at approximately 7 years, and enterprise adoption occurring within the last 5 years. Given the differences in maturity, NoSQL tools aren’t going to have the same level of maturity as those of Relational DB’s.

When evaluating new DB technologies, you need to understand the tradeoffs and what you’re willing to give up – whether it be data consistency, availability, or other features core to the DB – and determine if the benefits outweigh the tradeoffs. And all these DB’s aren’t created equally – they’re built off of different models for data store and access, use different language – which all require a ramp up.

In addition, keep in mind that scale and speed are all relative to your needs. Understanding all of these factors in the front end will help you make the right decision for the near and long term.

Questions to Ask Yourself

If you’re trying to determine if NoSQL would be a good fit for a new application you’re designing, here are some questions to ask yourself:

  1. Will the requirements evolve? Most likely they will, rarely are all requirements provided upfront.
  2. Do I understand the tradeoffs? Understand your must have vs. like to have.
  3. What are the expectations of the data and patterns? Read vs. write, and how you handle analytics (understand operational vs. analytics DB and where the overlap is)
  4. Build vs. Buy behavior? Understand what you’re working with internally and that changing internal culture is a process
  5. Is the ops team on board? When introducing new DB technologies, it’s much easier when the ops team is on board to make sure the tools are properly optimized.

Schema Design Tidbits

Schema is one the most critical things to understand when designing applications for these new databases. Ultimately the data access patterns should drive your design. We’ll use MongoDB and Cassandra as examples as they’re leading NoSQL databases with different models.

When designing your schema for MongoDB, it’s important to balance your app needs, performance and data retrieval. Your schema doesn’t have to be defined day 1, which is a benefit of MongoDB’s flexible schema. MongoDB also contains collections, which are similar to tables in relational DB’s, where documents are stored. However, the collections don’t enforce structure. In addition, you have the option of embedding data within a document, which depending on your use case, could be highly recommended.

Another technology to think about is Cassandra, a wide column database where you model around your queries. By understanding the access patterns, and the types of questions your users are asking the DB, then you can design your schema to be more accurate. You also want to distribute data evenly across nodes. Lastly, you want to minimize partition (groups of rows that share the same key) reads.

Architecture Examples

MongoDB has a primary-secondary architecture, where the secondary would become the primary if it ever failed, resulting in the notion of never having a DB offline. There are also rights, consistency, and durability, with primaries replicating to the secondaries. So in this model, the database is always available, where data is consistent and replicated across nodes, all performed in the backend by MongoDB. In terms of scalability, you’re scaling horizontally, with nodes being added as you go, which introduces a new concept of sharding, involving how data dynamically scales as the app grows.

On the other hand, Cassandra has a ring-based architecture, where data is distributed across nodes, similar to MongoDB’s sharding. There are similar patterns, but implemented differently within technologies. The diagram below illustrates architectural examples of MongoDB and Cassandra. All of these can be distributed globally, with dynamic scalability, the benefit being you can add nodes effortlessly as you grow.

NoSQL Data Solution Examples

Some of the NoSQL solutions we’ve recently built include:

Data Hub (aka 360 view, omni-channel) – A collection of various data sources pooled into a central location (in this case we used MongoDB), where use cases are built around the data. This enables new business units to access data they might not previously have access to, empowering them to build new products, understand how other teams operate, and ultimately lead to new revenue generating opportunities and improved processes across the organization

User Generated Content (UGC) & Analytics – Storing UGC sessions (e.g. blog comments and shares) that need to be stored and analyzed in the backend. A lot of times the Document model makes sense for this type of solution. However, as technologists continue to increase their NoSQL skill sets, there’s going to be an increasing amount of overlap of similar uses cases being built across various NoSQL DB types.

User Data Management – Also known as Profile Management, and storing information about the user, what they recently viewed, products bought, etc. With a Document model, the flexibility really becomes powerful to evolve the application as you can add attributes as you go, without the need to have all requirements defined out of the gate.

Lessons Learned

When talking about successful deployments, some of the lessons learned we’ve noticed include:

  1. Schema design is an ongoing process – From a Data Hub perspective, defining that “golden record” is not always necessary, as long as you define consistent fields that can be applied everywhere.
  2. Optimization is a team effort – It’s not just the developer’s job to optimize the schema, just like it’s not just the Ops team’s job to make sure the DB is always on. NoSQL is going to give you tunability across these, and the best performance and results
  3. Test your shard keys (MongoDB) – If sharding is a new concept for you, make sure you do your homework, understand and validate with someone that knows the DB very well.
  4. Don’t skimp on testing and use production data – Don’t always assume that the outcome is going to be the same in production.
  5. Shared resources will impact performance – Keep in mind if you’re deploying in the cloud that shared resources will impact distributed systems. This is where working with your Ops team will really help and eliminate frustrations.
  6. Understand what tools are available and where they are in maturity – Don’t assume existing tools (reporting, security, monitoring, etc.) will work in the same capacity as with Relational DB’s, and understand the maturity of the integration.
  7. Don’t get lost in the hype – Do your homework.
  8. Enable the “data consumer” – Enable the person that’s going to interact with the DB (e.g. data analyst) to make them comfortable working with the data.
  9. JSON is beautiful

To summarize, education will eliminate hesitation, and don’t get lost in the marketing fluff. Get Ops involved, the earlier and more often you work with your Ops team, the easier and more successful your application and your experience with these technologies will be. Lastly, keep in mind that these are just DB tools, so you’ll still need to build a front end.

Click here to see a recording of the webinar.

Click here for the webinar slides.