There is no such thing as a one-size-fits-all data warehouse. To that end, there is no singular approach to getting started. Getting started depends on your goals, needs, and where you are today. In this blog post, we’ll outline a few options 2nd Watch offers for getting started with a modern data warehouse and the details for each.
Option 1: Data Architecture Whiteboard Session
Option 2: Modern Data Warehouse Strategy Session
Option 3: Modern Data Warehouse Quickstart
Option 1: 60-Minute Data Architecture Assessment
A 60-minute data architecture assessment is a great option to see how a modern data warehouse would fit in your current environment and what would be involved to get from where you are now to where you want to be.
During this session, we will outline a plan to achieve your goals and help you understand the tools, technologies, timeline, and cost to get there.
Who is this for? Organizations in the very early planning stages
In order to see ROI and business value from your modern data warehouse, you must have a clear plan on how you are going to use it. During a modern data warehouse strategy project, our team will work with your stakeholders to understand your business goals and design a tech strategy to ensure business value and ROI from your data environment.
Who is this for? Organizations in the early planning stages looking to establish the business use case, cost benefits, and ROI of a modern data warehouse before getting started
Duration: 2-, 4-, 6-, or 8-week strategies are available
Not sure where to begin? We recommend beginning with a 60-minute data architecture assessment. This session allows us to walk through your current architecture, understand your organization’s pain points and goals for analytics, brainstorm on a future state architecture based on your goals, and then come up with next steps. Furthermore, the assessment allows us to determine if your organization needs to make a change, what those changes are, and how you might go about implementing them. Simply put, we want to understand the current state, learn about the future state of what you want to build toward, and help you create a plan so you can successfully execute on a modern data warehouse project.
A Word of Warning
Modern data warehouses are a big step forward from traditional on-premise architectures. They allow organizations to innovate quicker and provide value to the business much faster. An organization has many options in the cloud and many vendors offer a cloud data warehouse, but be careful: building a modern data warehouse architecture is highly involved and may require multiple technologies to get you to the finish line.
The most important thing to do when embarking on a modern data warehouse initiative is to have an experienced partner to guide you through the process the right way from establishing why a cloud data warehouse is important to your organization to outlining what the future state vision should be to develop a plan to get you there.
Data warehouse architecture is changing, don’t fall behind your competition! With multiple options for getting started, there is no reason to wait.
We hope you found this information valuable. If you have any questions or would like to learn more, please contact us and we’ll schedule a time to connect.
Dashboards have the power to quickly unlock information hidden in mountains of data and instrumentally inform key business decisions. Far too often, businesses overlook simple design elements that make these benefits almost completely unattainable. Designing effective dashboards requires an immense amount of attention to detail and communicating with multiple types of users. With so much to consider, key design standards are ignored too frequently. Whether you are beginning on your strategy or looking for ways to improve existing reports, these five tips will help you receive a high ROI on reporting and drive value through dashboard design.
1. Create multi-purpose reports by adding intuitive filters and drill-downs that reduce time spent searching for information.
Repetitive dashboards require more storage space and increase the time users spend navigating reports. Business users should not feel like they are searching for a needle in a haystack each time they want to include a metric. Multi-purpose reports fix this by reducing repetition of metrics so users don’t spend more time searching than they do analyzing.
Filters allow you to combine detailed and granular reports that focus on the same metrics. For example, if you have several sales reports focused on regions and stores, try combining them into a single dashboard. By using filters to aggregate metrics at either a store or region level, both sets of data are available in one place. Even better, this ensures that users rely on more consistent data.
The filters on this dashboard lead to a flexible interface that allows users to switch between levels of data aggregation, essentially providing multiple dashboards in one.
2. Ensure your reports lead to accurate conclusions by highlighting only the most important metrics in a clean and well-organized format.
Creating clean and concise dashboards is much easier said than done. Doing so requires skill and thoughtful discussions with business users. Cluttered or poorly designed dashboards often place the burden of determining the key information on users. This wastes time and energy. Even worse, it could lead to them drawing the wrong conclusions and focusing their efforts on a misled analysis.
Put in the time up front to determine what is most important. Then, organize your dashboard to communicate this to your users. One of the easiest tricks is to place the most essential information in the upper left hand corner of your dashboard. Users will naturally look here first because it follows how we read.
In this dashboard, high-level KPIs are found along the top and the left side. The placement and large size of these numbers make key metrics stand out and leave no question as to what is most important.
3. Centralizing reports helps communicate their context to business users and keeps them aware of the overall big picture.
Not only must dashboards showcase the right information, but to ensure they remain in context, they must be located in the right place as well. The most accurate decisions are made when users have the entire picture. This is often lost when users flip between applications or reports to find relevant information. If reports are located in multiple places, it becomes challenging to understand how they relate or whether the data points are even connected at all. This can lead to inaccurate assumptions and decreased productivity.
One of the easiest solutions to drive value through dashboard design is to use embedded analytics, which allows you to surface reports directly in your internal applications. Alternatively, a centralized reporting portal enables users to access all your reports from a single location.
4. Ensuring your BI notifications are clear and attention-grabbing reduces the chance of losing time-sensitive opportunities uncovered by data analytics.
Cost-saving design standards go beyond graphs and color choices. Notifications are essential for decision-makers to respond to business events in a timely manner. If events such as a shipping delay for a high-priority client go unnoticed, you could miss the opportunity to respond in a timely manner. As a result, the ROI on your BI investments, and potentially your customer relationships, will suffer.
To ensure your notifications receive effective responses, keep the message short, clear, and to the point. BI notifications should make the user aware they need to analyze relevant data, not spell out the details and outcome of the analysis. You should limit notifications only to key updates so users aren’t spammed every time a data point changes. Additionally, notifications should be delivered in a manner consistent with company communication practices to ensure they are received and acknowledged. Fortunately, many new BI tools such as Looker have built-in alerts that can notify you quickly and painlessly.
5. Spend time on your user experience design. If a dashboard is not easier to use than the processes already in place, users will be frustrated.
A poor user experience is the easiest way to tank the success of any reporting initiative. A bad design will, at best, frustrate your individual contributors – and at worst, paying customers. If business users cannot do their jobs correctly, productivity will nosedive. Not to mention, distrust for your analytics solution will skyrocket. Even worse, if paying customers are frustrated, you could see a direct hit to your revenues.
Dashboards with the best user experience often feature clean, responsive designs that feel modern and easy to use. If the dashboard is intuitive enough, it can impress even the most skeptical of users. This typically requires a strong analytics strategy with training and rollout plans to prevent frustration before it can even begin. A great tip to drive value through dashboard design is to include business users in the development process. This fosters buy-in and allows their needs to be better addressed.
2nd Watch has a vast range of experience in assisting companies with implementing all of these money-saving tricks and more. Whether you’re now inspired to eliminate repetitive reports, or are looking to implement these strategies earlier on, 2nd Watch can help you determine how to implement cost-saving practices in your analytics strategy. Contact us to get started.
The scales have finally tipped! According to a Flexera survey, 93% of organizations have a multi-cloud strategy and 53% are now operating with advanced cloud maturity. For those who are now behind the bell curve, it’s a reminder that keeping your data architecture in an on-premises solution is detrimental to remaining competitive. On-prem architecture restricts your performance and the overall growth and complexity of your analytics. Here are some of the setbacks of remaining on-prem and the benefits of data migration from legacy systems.
For most organizations, data architecture did not grow out of an intentional process. Many on-prem storage systems developed from a variety of events ranging from M&A activity and business expansion to vertical-specific database initiatives and rogue implementations. As a result, they’re often riddled with data silos that prevent comprehensive analysis from a single source of truth.
When organizations conduct reporting or analysis with these limitations, they are at best only able to find out what happened – not predict what will happen or narrow down what they should do. The predictive analytics and prescriptive analytics that organizations with high analytical maturity are able to conduct are only possible if there’s a consolidated and comprehensive data architecture.
Though you can create a single source of data with an on-prem setup, a cloud-based data storage platform is more likely to prevent future silos. When authorized users can access all of the data from a centralized cloud hub, either through a specific access layer or the whole repository, they are less likely to create offshoot data implementations.
Slower Query Performance
The insights from analytics are only useful if they are timely. Some reports are evergreen, so a few hours, days, or even a week doesn’t alter the actionability of the insight all that much. On the other hand, real-time analytics or streaming analytics requires the ability to process high-volume data at low latency, a difficult feat for on-prem data architecture to achieve without enterprise-level funding. Even mid-sized businesses are unable to justify the expense – even though they need the insight available through streaming analysis to keep from falling behind larger industry competitors.
Using cloud-based data architecture enables organizations to access much faster querying. The scalability of these resources allows organizations of all sizes to ask questions and receive answers at a faster rate, regardless of whether it’s real-time or a little less urgent.
Plus, those organizations that end up working with a data migration services partner can even take advantage of solution accelerators developed through proven methods and experience. Experienced partners are better at avoiding unnecessary pipeline or dashboard inefficiencies since they’ve developed effective frameworks for implementing these types of solutions.
More Expensive Server Costs
On-prem data architecture is far more expensive than cloud-based data solutions of equal capacity. When you opt for on-prem, you always need to prepare and pay for the maximum capacity. Even if the majority of your users are conducting nothing more complicated than sales or expense reporting, your organization still needs the storage and computational power to handle data science opportunities as they arise.
All of that unused server capacity is expensive to implement and maintain when the full payoff isn’t continually realized. Also, on-prem data architecture requires ongoing updates, maintenance, and integration to ensure that analytics programs will function to the fullest when they are initiated.
Cloud-based data architecture is far more scalable, and providers only charge you for the capacity you use during a given cycle. Plus, it’s their responsibility to optimize the performance of your data pipeline and data storage architecture – letting you reap the full benefits without all of the domain expertise and effort.
Hindered Business Continuity
There’s a renewed focus on business continuity. The recent pandemic has illuminated the actual level of continuity preparedness worldwide. Of the organizations that were ready to respond to equipment failure or damage to their physical buildings, few were ready to have their entire workforce telecommuting. Those with their data architecture already situated in the cloud fared much better and more seamlessly transitioned to conducting analytics remotely.
The aforementioned accessibility of cloud-based solutions gives organizations a greater advantage over traditional on-prem data architecture. There is limited latency when organizations need to adapt to property damage, natural disasters, pandemic outbreaks, or other watershed events. Plus, the centralized nature of this type of data analytics architecture prevents unplanned losses that might occur if data is stored in disparate systems on-site. Resiliency is at the heart of cloud-based analytics.
It’s time to embrace data migration from legacy systems in your business. 2nd Watch can help! We’re experienced with migration legacy implementations to Azure Data Factory and other cloud-based solutions.