Real-time analytics is a discipline that revolves around swiftly processing and interpreting data as it’s generated, providing instant insights and actionable information useful for improving business decisions. In contrast, traditional analytics relies on batch processing, leading to delayed results. Real-time analytics empowers businesses, industries, and even sports venues to gain a competitive edge, optimize operations, and elevate customer experiences.
To demonstrate its practical application, let’s transport ourselves to Wrigley Field on a Sunday afternoon, where the Chicago crosstown rivals are about to compete. As fans eagerly enter the ballpark, an advanced fan occupancy dashboard diligently tracks each entry into the venue. This real-time data collection and analysis play a pivotal role in ensuring a seamless and enjoyable experience for both fans and event organizers.
Assess Your Infrastructure for Scalability
To successfully implement real-time analytics, organizations – including professional baseball teams – must establish a scalable data infrastructure. Creating a scalable data infrastructure involves building a skilled team of data engineers and selecting the appropriate technology stack. Before delving into real-time analytics, it’s crucial for organizations to conduct a thorough assessment of your current infrastructure.
This assessment entails evaluating the scalability of existing data systems to ascertain their ability to handle the growing volumes of data. Moreover, the data processing and storage systems, including cloud data warehouses, must demonstrate resilience to manage the continuous influx of data without compromising performance. By ensuring a robust and scalable data infrastructure, organizations can lay the groundwork for effective real-time analytics and gain valuable insights from high-velocity data streams. This also applies to incoming data. An organization’s ability to make decisions will be impacted by how quickly they can factor in new information as it arises; thus, being able to ingest large amounts of data as soon as it becomes available is a vital capability.
Ensure Data Quality and Governance
As an organization begins to ingest and process data in real-time, a standardized approach to data governance becomes essential. Data governance is the process of creating an accountability framework and designating decision rights around an organization’s data with the intention of ensuring the appropriate creation, consumption, and management thereof. Users need access to relevant, high-quality data in a timely manner so they can take action. By implementing data governance policies, organizations can define metrics around data accuracy and work to improve those.
Starting a data governance process requires first identifying essential data. A retail company, for instance, may consider customer purchase patterns as key user behavior intel. Maintaining data integrity, using strategies like automated validation rules for data accuracy, is vital to protect this historical data and ensure its usefulness going forward. Setting measurable metrics and monitoring adherence helps in maintaining quality. If data errors exceed a set limit, it triggers a data cleaning process.
Identifying authority for final decisions on data, like a chief data officer or a data governance board, is essential. Their authority should reflect in data access permissions, limiting who can change or view sensitive data. When implementing data governance policies, the organization must consider the type of stored information, its intended use, and the user type. These factors impact data security, privacy, and regulatory compliance.
Confirm Resource Availability
Skilled personnel are equally as important, if not more so, than the foundation of infrastructure and data governance practices. An organization needs to assess if their IT team has the capacity to maintain the tools and processes surrounding real-time analytics. IT personnel must be able to ingest and process this data for instant consumption in a sustainable manner to gain maximum value.
Additionally, “skilled” is a keyword in “skilled personnel.” Does your IT team have the knowledge and experience to handle real-time data analytics, or do you need to look into hiring? Is there someone on the team who can help with upskilling other staff? Make sure you have this people-focused infrastructure in place in conjunction with your data infrastructure.
Identify Business Use Cases
In situations that demand swift decision-making based on extensive data, an organization can realize considerable advantages through the use of real-time analytics. Instantaneous insights derived from data equip businesses to adjust to rapid market changes and strategically place themselves for prosperity.
Pivoting back to Wrigley Field, tracking fan turnout is simply one among potentially 100 business circumstances where real-time analytics can demonstrate its value. The home team’s concession management can promptly assess sales of merchandise and concessions, and they can begin amending their forecast for the next day’s game right away. In tandem, their chief marketing officer could fine-tune marketing strategies based on ticket sale trends, consequently improving stadium fill rates. Beyond that, there are opportunities to delve into game-generated data and player statistics to understand their potential effects on audience behavior.
Furthermore, keep in mind the impact of data lag when you’re exploring your industry or business for typical or standard operations that suffer due to a delay in data access. How about fraud detection? Or even using the power of streaming data to enable enhanced business intelligence, predictive analytics, and machine learning? Identifying these situations will be key in assisting you to unearth the most effective applications of real-time analytics within your enterprise.
Consider Security and Compliance
Whenever changes are made to your digital framework, it’s crucial to tackle possible security threats. Your organization needs to understand the nature of the sensitive data it holds and who has the right to access it. For example, think about a healthcare company managing patient data. There is a necessity for strict controls over access to such sensitive data. The company must ensure that only individuals with the right authorization can access this information. Moreover, they should be thorough in overseeing their cloud service provider and any other related entities that might handle or use this data. This approach safeguards individual privacy and adheres to regulatory standards like HIPAA in the United States.
Depending on the specifics of the data, infrastructure adjustments may also be required to keep in line with data protection rules. Using our Wrigley Field example, there may be collection of personal financial information through ticket and concession sales. In these circumstances, it’s critical to ensure that this data is handled securely and in compliance with all appropriate regulations.
Evaluate Financial Implications and ROI
A crucial aspect of this evaluation involves analyzing the expenses and the ROI associated with the adoption of real-time analytics. There could be monetary considerations related to storage and computational costs, as well as the potential need for more personnel. These factors can fluctuate based on an organization’s existing infrastructure, the skill level of its employees, and the complexity and amount of data to be processed. All these elements need to be balanced against the anticipated ROI and enduring advantages, both quantifiable and qualitative.
Does faster response time decrease operational expenses, enhance customer interactions, or even mitigate security threats? By optimizing operations and reacting swiftly to market fluctuations, organizations can reap significant financial rewards.
Embrace and Implement Real-Time Analytics
Once an organization recognizes an opportunity to apply real-time analytics, the next phase involves identifying and evaluating the data sources that can facilitate this implementation. Subsequently, the organization needs to manage data ingestion, processing, and storage, before defining and constructing any final products. During each of these phases, the choice of suitable tools and technologies is crucial. Your organization should take into account your current infrastructure, maintenance requirements, team skill sets, and any fresh data you wish to integrate into your solution.
Consequently, real-time analytics can give your organization a distinct advantage by allowing data processing as soon as it’s generated, leading to swift and well-informed decision-making. A well-executed implementation has the potential to help anticipate significant issues, boost predictability, optimize operations, and enhance customer relations. Given our society’s data-rich environment, organizations can harness this asset to produce improved solutions and customer experiences. Ready to take action but unsure of the initial steps? Contact 2nd Watch for a complimentary real-time analytics roadmap whiteboarding session.
Private equity (PE) data management and the ability to leverage PE big data effectively and efficiently are of growing importance for firms seeking long-term growth and sustainability. As the number of firms grows, the opportunity cost of not utilizing data-driven decision-making in private equity could be the difference between success and failure.
Acquisitions and ongoing tracking of portfolio companies are data-dense and can be labor-intensive if firms aren’t properly leveraging the tools at their disposal, but data integration doesn’t happen overnight. Well-thought-out data strategy in private equity can position firms to use data most effectively to drive growth and maximize returns within their portfolio of companies.
In building out a data strategy framework, the goal of any firm is to create on-demand analytics that can provide real-time insights into the performance of their portfolio companies. Data integration in private equity is an ongoing priority as each new acquisition comes with new data sources, metrics, definitions, etc., that can create complications in proper reporting, especially in the case of roll-ups.
Private equity big data and analysis provide an opportunity for firms to more effectively and efficiently measure success, track financial performance, improve operations, and much more. Generating a data-focused strategy is the first step for firms looking to generate on-demand analytics and reporting; and it requires them to define the technology, processes, people, and private equity data governance and data security required to manage all assets effectively and safely.
Key Components of Private Equity Data Strategy
Data optimization for private equity is key as firms continue to expand acquisitions, but this can only be achieved through the development of a sound data strategy. It requires front-end work to get off the ground; however, the benefits of PE digital transformation much outweigh what can sometimes be seen as a daunting task.
These benefits include a clarified vision across the organization and the ability to plan and budget effectively in alignment with this vision – accounting for anticipated challenges and risks. Additionally, private equity portfolio data analysis allows for smarter and more educated buying decisions by leveraging real-time data and market insights. The broad usability of data and analytics tools drives adoption and encourages change across the organization which translates to increased success for firms. Best of all, a clear and well-executed data strategy allows everyone to focus on a single source of truth with the transparency that effective data integration offers.
Each private equity firm will require a unique strategy that is catered to its needs and the needs of its portfolio of companies. Regardless of a specific firm’s needs, there are individual tools and tools that can combine to perform the necessary functions to improve the processes of any firm in question.
Financial Reporting
In the case of firms conducting a roll-up, the financial reporting process that comes with the consolidation can be a bear. With many firms still relying on manual reporting processes, slow and inconsistent reporting can translate to slow and inconsistent decision-making. Firms in this position need the ability to execute quick and easy portfolio data analysis to understand their financial performance, and many have realized that without harnessing tools to ease this process, their current approach is neither practical nor scalable as they continue to seek out additional acquisitions.
With clearly defined goals and the knowledge of how to leverage applicable PE data platforms, firms can improve their processes and optimize them in a scalable way. There are four steps for a firm in this position to streamline its financial reporting during the roll-up process.
The first is some level of restructuring of the data into a format that supports accurate reporting of pre- and post-acquisition date ranges. From there, a model is implemented to effectively manipulate data among necessary databases. Next comes data consolidation: data mining and machine learning in private equity assist in financial consolidation and reporting by connecting multiple functions, accelerating the speed and accuracy with which data can be processed.
Finally, through custom dashboard creation, firms can leverage more effective data visualization in private equity to provide an in-depth and interactive view for any members of the organization – in this case, financial advisors. With the front-end work in place, ongoing management and development are made much easier with streamlined processes for adding additional acquisitions to the data management platform.
Portfolio Data Analysis and Consolidation
Similarly, PE roll-ups naturally lead to an influx of data from each target company. Firms need a way to effectively and efficiently consolidate data, standardize KPIs across the organization, and analyze data in a user-friendly way. This provides an opportunity to take advantage of private equity business intelligence to improve operations, financial performance, and cash-flow predictability.
Clear strategy and design aligned with the firm and catering of the solution to their long-term growth are essential in any successful digital transformation. The next step involves the centralization of data from multiple source systems through the implementation of custom data pipelines to a centralized data warehouse. Once there, it’s time to leverage tools to organize, standardize, and structure the data to ensure consistency and preparedness for analytics.
At this point, it’s up to firms to create interactive, user-friendly dashboards to easily visualize KPIs and track performance across individual companies within their portfolio and organizations as a whole. By leveraging AI in private equity, firms can create smart reports that fit the needs of any queries they may be interested in improving or learning more about. When PE firms become more effective at analyzing specific data, they position themselves to make more well-educated and efficient decisions as they continue to build their portfolio.
Predictive Analytics
In certain cases, data analytics for private equity can be leveraged to improve portfolio companies. By using predictive analytics in private equity, firms can forecast future trends and performance to make more accurate predictions about opportunities and risks.
In seasons of fast-paced growth, the ability to automatically aggregate and evaluate real-time incoming data, leveraging AI and machine learning, can allow for smarter and faster decision-making that translates to increased growth. Some firms and target companies that are still utilizing manual processes can exponentially increase their bandwidth by leveraging these tools. By combining inputs into a centralized data hub, many processes can be automated and run simultaneously to optimize efficiency and scalability. By connecting these different tools and processes, data is more accurate and more quickly available. This allows for significantly increased output, translating to smarter and faster decision-making that makes scaling effortless when compared to the processes prior.
What This Means for Private Equity Firms
Now more than ever, it’s crucial for PE firms to understand the opportunities presented by developing an optimized data strategy. In an increasingly competitive environment, a data strategy could be the difference between continued growth or failure as other firms adopt these practices.
Leveraging data science in private equity can be daunting and confusing, especially if you are trying to tackle it yourself. At 2nd Watch, we have a team who is ready to help you understand how your firm can benefit from these tools and help you implement them to continue to accelerate your growth trajectory. Many of these examples were pulled directly from our work with other clients, so whether you find yourself facing similar challenges or something unique to your specific situation, we are confident we can help find and create a solution that’s right for you. To begin building a solution aligned with your firm’s vision and continued growth, contact us today for a Private Equity Data Strategy Whiteboarding Session.
Private equity firms are increasingly looking to digital transformation to create value and remain competitive in an ever-changing market. The digital transformation process revolutionizes companies with a range of innovative tech: big data analytics, artificial intelligence, machine learning, the Internet of Things, blockchain, cloud solutions, Software as a Service (Saas), cloud computing, and more. Relative to other industries moving toward digitalization, private equity firms have been considered late movers with the exceptions of large firms, like Blackstone and The Carlyle Group, who have given themselves an edge with the early interest they’ve taken.
Traditionally, PE firms have made investment decisions based on cyclical data sources such as quarterly earnings, financial reports, tax filings, etc., that could be limited in terms of scope and frequency. With increasing competition in the private equity sector, digital transformation provides an opportunity for firms to make real-time assessments and avoid falling behind the competition.
Specifically within private equity, firms seek to leverage these technologies to improve operational efficiency, streamline processes, and enhance the overall value of portfolio companies. Along with the improvement of portfolio companies, firms applying these technologies internally position themselves as well as possible to adapt to the quickly changing, increasingly competitive standards that are being called upon for survival in the private equity industry.
This blog post will highlight best practices that PE firms can utilize during the digital transformation process and analyze the value-creation opportunities that forward-thinking firms can take advantage of by leaning into implementation.
Best Practices for Digital Transformation in Private Equity
Before taking on any digital transformation initiatives, PE firms should have a clear transformation strategy outlining their objectives, priorities, and timelines while taking into account the unique characteristics of their portfolio companies and the industry landscape. While the digital transformation process doesn’t need to be complicated, it is critical that firms are strategic in how they carry out implementation. As a firm, the most valuable reason for transformation is the ability to convert acquisitions into data-driven smart companies.
Operating partners are playing an increasingly important role in this process as their close work with portfolio companies allows them to help identify opportunities, assess risk, and execute initiatives aligned with the digital transformation process. Getting them involved early in the process can allow firms to retain valuable input and buy-in while also helping to build necessary capabilities within the portfolio companies.
Due Diligence
Within the due diligence process, firms must be able to identify areas where potential acquisitions can benefit most from digital transformation.
From a production side, firms have the ability to investigate supplier evaluations and see the production process, in real-time, to identify bottlenecks and opportunities to optimize workflow. Additionally, firms can evaluate inventory tracking and the potential to optimize working capital, track customer satisfaction to facilitate omnichannel sales and personalized marketing, and perform automated analysis for fund managers to judge the feasibility and profitability of new products and business models the target company aims to promote.
It is critical for private equity firms to leverage the correct digital and industry 4.0 technologies to maximize value creation within their portfolio of companies.
Digital investment is a key part of firms’ digital transformation strategies positioning them to disrupt traditional industries, improve efficiency, and enhance the value of their portfolio companies. The specific modalities and use cases for digital technology depend largely on the target company in question:
Artificial Intelligence & Machine Learning: These technologies are becoming increasingly important and can allow firms to better understand and analyze data, identify new investment opportunities, and improve operational efficiency within portfolio companies.
Big Data Analytics: With access to vast amounts of data, firms can acquire insights into market trends, customer behavior, and other key metrics to drive growth and innovation.
E-Commerce & Fintech: With the rise of online shopping and digital payments, these industries are experiencing significant growth and disruption, making them attractive targets for investment and tools for streamlining processes.
Blockchain: Firms are still beginning to explore this technology that offers the potential to revolutionize the way transactions are conducted, making them faster, more secure, and more transparent.
SaaS: This technology offers the ability to deliver software and other digital products over the internet, making it easier and more cost-effective for private equity firms to adopt new technologies and stay competitive.
Industry 4.0: Technologies like the Internet of Things (IoT), 5G, and edge computing are transforming how businesses operate. This provides private equity firms with an opportunity to improve efficiency, reduce cost, and enhance the customer experience within portfolio companies.
Value Creation
Digital transformation in private equity firms provides an exceptional opportunity to leverage technology and create value in portfolio companies. By strategically leveraging the innovations at their disposal, firms are able to improve target companies in a variety of ways:
Operational Efficiency:
Private equity firms can use digital technology to streamline their portfolio companies’ operations and improve efficiency. For example, by implementing automation and machine learning solutions, firms can automate repetitive tasks and improve decision-making based on data analysis. This can reduce costs, increase productivity, and even improve profitability.
Customer Experience:
Digital transformation can enable firms to leverage big data and AI to gain insights into customer preferences and behavior. By using this information, private equity firms can create offerings within their portfolio companies that are personalized to their customer base and improve customer engagement and overall customer experience.
Accelerated Growth:
Through digitalization, private equity firms can accelerate the growth of their portfolio companies by allowing them to quickly scale operations and enter new markets. Implementing cloud computing and SaaS solutions can assist companies in rapidly deploying new products and services, expanding their customer base.
These are just a few examples of how private equity firms can create value through digital transformation – internally and within their portfolio companies. Building a solid understanding of the opportunities that technological innovations have presented for private equity firms could be the difference between sinking or swimming in an increasingly competitive market.
2nd Watch partners with private equity firms to help them understand and execute their digital transformation strategy to ensure they are equipped to continue growing and separating themselves from the competition. With expertise in cloud migration and management services, we’re well-versed in the most effective ways PE firms can leverage digital transformation to create value. If you’re aware of the opportunity that digital transformation presents but feel like you could benefit from expert guidance, a whiteboard session with the 2nd Watch team is a great place to start. To learn more and begin the digital transformation process for your firm, contact us today and we can get started.
Private equity funds are shifting away from asset due diligence toward value-focused due diligence. Historically, the due diligence (DD) process centered around an audit of a portfolio company’s assets. Now, private equity (PE) firms are adopting value-focused DD strategies that are more comprehensive in scope and focus on revealing the potential of an asset.
Data analytics are key in support of private equity groups conducting value-focused due diligence. Investors realize the power of data analytics technologies to accelerate deal throughput, reduce portfolio risk, and streamline the whole process. Data and analytics are essential enablers for any kind of value creation, and with them, PE firms can precisely quantify the opportunities and risks of an asset.
The Importance of Taking a Value-Focused Approach to Due Diligence
Due diligence is an integral phase in the merger and acquisition (M&A) lifecycle. It is the critical stage that grants prospective investors a view of everything happening under the hood of the target business. What is discovered during DD will ultimately impact the deal negotiation phase and inform how the sale and purchase agreement is drafted.
The traditional due diligence approach inspects the state of assets, and it is comparable to a home inspection before the house is sold. There is a checklist to tick off: someone evaluates the plumbing, another looks at the foundation, and another person checks out the electrical. In this analogy, the portfolio company is the house, and the inspectors are the DD team.
Asset-focused due diligence has long been the preferred method because it simply has worked. However, we are now contending with an ever-changing, unpredictable economic climate. As a result, investors and funds are forced to embrace a DD strategy that adapts to the changing macroeconomic environment.
With value-focused DD, partners at PE firms are not only using the time to discover cracks in the foundation, but they are also using it as an opportunity to identify and quantify huge opportunities that can be realized during the ownership period. Returning to the house analogy: during DD, partners can find the leaky plumbing and also scope out the investment opportunities (and costs) of converting the property into a short-term rental.
The shift from traditional asset due diligence to value-focused due diligence largely comes from external pressures, like an uncertain macroeconomic environment and stiffening competition. These challenges place PE firms in a race to find ways to maximize their upside to execute their ideal investment thesis. The more opportunities a PE firm can identify, the more competitive it can be for assets and the more aggressive it can be in its bids.
Value-Focused Due Diligence Requires Data and Analytics
As private equity firms increasingly adopt value-focused due diligence, they are crafting a more complete picture using data they are collecting from technology partners, financial and operational teams, and more. Data is the only way partners and investors can quantify and back their value-creation plans.
During the DD process, there will be mountains of data to sift through. Partners at PE firms must analyze it, discover insights, and draw conclusions from it. From there, they can execute specific value-creation strategies that are tracked with real operating metrics, rooted in technological realities, and modeled accurately to the profit and loss statements.
This makes data analytics an important and powerful tool during the due diligence process. Data analytics can come in different forms:
Data Scientists:PE firms can hire data science specialists to work with the DD team. Data specialists can process and present data in a digestible format for the DD team to extract key insights while remaining focused on key deal responsibilities.
Data Models: PE firms can use a robustly built data model to create a single source of truth. The data model can combine a variety of key data sources into one central hub. This enables the DD team to easily access the information they need for analysis directly from the data model.
Data Visuals: Data visualization can aid DD members in creating more succinct and powerful reports that highlight key deal issues.
Document AI: Harnessing the power of document AI, DD teams can glean insights from a portfolio company’s unstructured data to create an ever more well-rounded picture of a potential acquisition.
Data Analytics Technology Powers Value
Value-focused due diligence requires digital transformation. Digital technology is the primary differentiating factor that can streamline operations and power performance during the due diligence stage. Moreover, the right technology can increase or decrease the value of a company.
Data analytics ultimately allows PE partners to find operationally relevant data and KPIs needed to determine the value of a portfolio company. There will be enormous amounts of data for teams to wade through as they embark on the DD process. However, savvy investors only need the right pieces of information to accomplish their investment thesis and achieve value creation. Investing in robust data infrastructure and technologies is necessary to implement the automated analytics needed to more easily discover value, risk, and opportunities. Data and analytics solutions include:
Financial Analytics: Financial dashboards can provide a holistic view of portfolio companies. DD members can access on-demand insights into key areas, like operating expenses, cash flow, sales pipeline, and more.
Operational Metrics:Operational data analytics can highlight opportunities and issues across all departments.
Executive Dashboards: Leaders can access the data they need in one place. This dashboard is highly tailored to present hyper-relevant information to executives involved with the deal.
At 2nd Watch, we can assist you with value-focused due diligence by providing comprehensive cloud cost analysis and optimization strategies. Here’s how we can help:
Cost Analysis: We conduct a thorough evaluation of your existing cloud infrastructure and spend. We analyze your usage patterns, resource allocations, and pricing models to identify areas of potential cost savings.
Optimization Strategies: Based on the cost analysis, we develop customized optimization strategies tailored to your specific needs. Our strategies focus on maximizing value and cost-efficiency without sacrificing performance or functionality.
Right-Sizing Recommendations: We identify instances where your resources are over-provisioned or underutilized. We provide recommendations to right-size your infrastructure, ensuring that you have the appropriate resource allocations to meet your business requirements while minimizing unnecessary costs.
Reserved Instance Planning: Reserved Instances (RIs) can offer significant cost savings for long-term cloud usage. We help you analyze your usage patterns and recommend optimal RI purchases, enabling you to leverage discounts and reduce your overall AWS spend.
Cost Governance and Budgeting: We assist in implementing cost governance measures and establishing budgeting frameworks. This ensures that you have better visibility and control over your cloud spend, enabling effective decision-making and cost management.
Ongoing Optimization: We provide continuous monitoring and optimization services, ensuring that your cloud environment remains cost-efficient over time. We proactively identify opportunities for further optimization and make recommendations accordingly.
By partnering with 2nd Watch, you can conduct due diligence with a clear understanding of your cloud costs and potential areas for optimization. We empower you to make informed decisions that align with your business goals and maximize the value of your cloud investments. Visit our website to learn more about how we can help with value-focused due diligence.
There are many options when it comes to data analytics tools. Choosing the right one for your organization will depend on a number of factors. Since many of the reviews and articles on these tools are focused on business users, the 2nd Watch team wanted to explore these tools from the developer’s perspective. In this developer’s guide to Power BI, we’ll go over the performance, interface, customization, and more to help you get a full understanding of this tool.
Why Power BI?
Power BI is a financially attractive alternative to the likes of Tableau and Looker, which either offer custom-tailored pricing models or a large initial per-user cost followed by an annual fee after the first year. However, don’t conflate cost with quality; getting the most out of Power BI is more dependent on your data environment and who is doing the data discovery. Companies already relying heavily on Microsoft tools should look to add Power BI to their roster, as it integrates seamlessly with SQL Server Analysis Services to facilitate faster and deeper analysis.
Performance for Developers
When working with large datasets, developers will experience some slowdown as they customize and publish their reports. Developing on Power BI works best with small-to-medium-sized data sets. At the same time, Microsoft has come out with more optimization options such as drill-through functionality, which allows for deeper analytical work for less processing power.
Performance for Users
User performance through Power BI Services is controlled through row-level security implementation. For any sized dataset, the number of rows can be limited depending on the user’s role. Overviews and executive dashboards may run somewhat slowly, but as the user’s role becomes more granular, dashboards will operate more quickly.
User Interface: Data Layer
Data is laid out in a tabular form; clicking any measure column header reveals a drop-down menu with sorting options, filtering selections, and the Data Analysis Expressions (DAX) behind the calculation.
User Interface: Relationship Layer
The source tables are draggable objects with labeled arrows between tables denoting the type of relationship.
Usability and Ease of Learning
Microsoft Power BI documentation is replete with tutorials, samples, quickstarts, and concepts for the fundamentals of development. For a more directed learning experience, Microsoft also put out the Microsoft Power BI Guided Learning set, which is a freely available collection of mini courses on modeling, visualization, and exploration of data through Power BI. It also includes an introduction to DAX development as a tool to transform data in the program. Additionally, the Power BI community forums almost always have an answer to any technical question a developer might have.
Modeling
Power BI can easily connect to multiple data sources including both local folders and most major database platforms. Data can be cleaned and transformed using the Query Editor; the Editor can change data type, add columns, and combine data from multiple sources. Throughout this transformation process, the Query Editor records each step so that every time the query connects to the data source, the data is transformed accordingly. Relationships can be created by specifying a from: table and to table, the keys to relate, a cardinality, and a cross-filter direction.
Customization
In terms of data transformation, Power Query is a powerful language for ensuring that your report contains the exact data and relationships you and your business user are looking to understand. Power Query simplifies the process of data transformation with an intuitive step-by-step process for joining, altering, or cleaning your tables within Power BI. For actual report building, Power BI contains a comprehensive list of visualizations for almost all business needs; if one is not found within the default set, Microsoft sponsors a visual gallery of custom user-created visualizations that anyone is free to explore and download.
Permissions and User Roles
Adding permissions to workspaces, datasets, and reports within your org is as simple as adding an email address and setting an access level. Row-level security is enabled in Power BI Desktop; role management allows you flexibly customize access to specific data tables using DAX functions to specify conditional filters. Default security filtering is single-directional; however, bi-directional cross-filtering allows for the implementation of dynamic row-level security based on usernames and/or login IDs.
Ease of Dev Opp and Source Control
When users have access to a data connection or report, source and version control are extremely limited without external GitHub resources. Most of the available activities are at the macro level: viewing/editing reports, adding sources to gateways, or installing the application. There is no internal edit history for any reports or dashboards.
Setup and Environment
Setup is largely dependent on whether your data is structured in the cloud, on-premises, or a hybrid. Once the architecture is established, you need to create “data gateways” and assign them to different departments and data sources. This gateway acts as a secure connection between your data source and development environments. From there, security and permissions can be applied to ensure the right people within your organization have access to your gateways. When the gateways are established, data can be pulled into Power BI via Power Query and development can begin.
Implementation
The most common implementation of Power BI utilizes on-premises source data and Power BI Desktop for data preparation and reporting, with Power BI Service used in the cloud to consume reports and dashboards, collaborate, and establish security. This hybrid implementation strategy takes advantage of the full range of Power BI functionality by leveraging both the Desktop and Service versions. On-premises data sources connect to Power BI Desktop for development, leading to quicker report creation (though Power BI also supports cloud-based data storage).
Summary and Key Points
Power BI is an extremely affordable and comprehensive analytics tool. It integrates seamlessly with Excel, Azure, and SQL Server, allowing for established Microsoft users to start analyzing almost instantly. The tool is easy to learn for developers and business users alike, and there are many available resources, like Microsoft mini-courses and community forums.
A couple things to be aware of with Power BI: It may lack some of the bells and whistles as compared to other analytics tools, and it’s best if you’re already in the Microsoft ecosystem and are coming in with a solid data strategy.
If you want to learn more about Power BI or any other analytics tools, contact us today to schedule a no-obligation whiteboard session.
Tableau gets a good reputation for being sleek and easy to use and by bolstering an impeccable UI/UX. It’s by and large an industry leader due to its wide range of visualizations and ability to cohesively and narratively present data to end users. As a reliable, well-established leader, Tableau can easily integrate with many sources, has extensive online support, and does not require a high level of technical expertise for users to gain value.
One of the easiest ways to ensure good performance with Tableau is to be mindful of how you import your data. Utilizing extracts rather than live data and performing joins or unions in your database reduces a lot of the processing that Tableau would otherwise have to do. While you can easily manipulate data without any coding, these capabilities reduce performance significantly, especially when dealing with large volumes of information. All data manipulation should be done in your database or data warehouse prior to adding it as a source. If that isn’t an option, Tableau offers a product called Tableau Prep that enables data manipulation and enhanced data governance capabilities.
Performance for Users
Dashboard performance for users depends almost entirely on practices employed by developers when building out reports. Limiting the dataset to information required for the goals of the dashboard reduces the amount of data Tableau processes as well as the number of filters included for front-end users. Cleaning up workbooks to reduce unnecessary visualizations will enhance front-end performance as well.
User Interface: Data Source
After connecting to your source, Tableau presents your data using the “Data Source” tab. This is a great place to check that your data was properly loaded and doesn’t have any anomalies. Within this view of the data, you have the chance to add more sources and the capability to union and join tables together as well as filter the data to a specific selection and exclude rows that were brought in.
User Interface: Worksheet
The “Worksheet” tabs are where most of the magic happens. Each visualization that ends up on the dashboard will be developed in separate worksheets. This is where you will do most of the testing and tweaking as well as where you can create any filters, parameters, or calculated fields.
User Interface: Dashboards
In the “Dashboard” tab, you bring together all of the individual visualizations you have created. The drag-and-drop UI allows you to use tiles predetermined by Tableau or float the objects to arrange them how you please. Filters can be applied to all of the visualizations to create a cohesive story or to just a few visualizations to break down information specific to a chart or table. It additionally allows you to toggle between different device layouts to ensure end-user satisfaction.
User Interface: Stories
One of the most unique Tableau features is its “Stories” capability. Stories work great when you need to develop a series of reports that present a narrative to a business user. By adding captions and placing visualizations in succession, you can convey a message that speaks for itself.
Usability and Ease of Learning
The Tableau basics are relatively easy to learn due to the intuitive point-and-click UI and vast amount of educational resources such as their free training videos. Tableau also has a strong online community where answers to specific questions can be found either on the Help page or third-party sites.
Creating an impressive variety of simple visualizations can be done without a hitch. This being said, there are a few things to watch out for:
Some tricks and more niche capabilities can easily remain undiscovered.
Complex features such as table calculations may confuse new users.
The digestible UI can be deceiving – visualizations often appear correct when the underlying data is not. One great way to check for accuracy is to right-click on the visualization and select “View Data.”
Modeling
Unlike Power BI, Tableau does not allow users to create a complicated semantic layer within the tool. However, users can establish relationships between different data sources and across varied granularities through a method called data blending. One way to implement this method is by selecting the “Edit Relationships” option in the data drop-down menu.
Data blending also eliminates duplicates that may occur by using a function that returns a single value for the duplicate rows in the secondary source. Creating relationships among multiple sources in Tableau requires attention to detail as it can take some manipulation and may have unintended consequences or lead to mistakes that are difficult to spot.
Customization
The wide array of features offered by Tableau allows for highly customizable visualizations and reports. Implementing filter actions (which can apply to both worksheets and dashboards), parameters, and calculated fields empowers developers to modify the source data so that it better fits the purpose of the report. Using workarounds for calculations not explicitly available in Tableau frequently leads to inaccuracy; however, this can be combated by viewing the underlying data. Aesthetic customizations such as importing external images and the large variety of formatting capabilities additionally allow developers boundless creative expression.
Permissions and User Roles
The type of license assigned to a user determines their permissions and user roles. Site administrators can easily modify the site roles of users on the Tableau Server or Tableau Online based on the licenses they hold. The site role determines the most impactful action (e.g., read, share, edit) a specific user can make on the visualizations. In addition to this, permissions range from viewing or editing to downloading various components of a workbook. The wide variety of permissions applies to various components within Tableau. A more detailed guide to permissions capabilities can be found here.
Ease of Dev Opp and Source Control
Dev opp and source control improved greatly when Tableau implemented versioning of workbooks in 2016. This enables users to select the option to save a history of revisions, which saves a version of the workbook each time it is overwritten. This enables users to go back to previous versions of the workbook and access work that may have been lost. When accessing prior versions, keep in mind that if an extract is no longer compatible with the source, its data refresh will not work.
Setup and Environment
With all of the necessary information on your sources, setup in Tableau is a breeze. It has built-in connectors with a wide range of sources and presents your data to you upon connection. You also have a variety of options regarding data manipulation and utilizing live or static data (as mentioned above). Developers utilize the three Tableau environments based primarily on the level of interactions and security they desire.
Tableau Desktop: Full developer software in a silo; ability to connect to databases or personal files and publish work for others to access
Tableau Server: Secure environment accessed through a web browser to share visualizations across the organization; requires a license for each user
Tableau Online: Essentially the same as Tableau Server but based in the cloud with a wider range of connectivity options
Implementation
Once your workbook is developed, select the server and make your work accessible for others either on Tableau Online or on Tableau Server by selecting “publish.” During this process, you can determine the specific project you are publishing and where to make it available. There are many other modifications that can be adjusted such as implementing editing permissions and scheduling refreshes of the data sources.
Summary and Key Points
Tableau empowers developers of all skill levels to create visually appealing and informative dashboards, reports, and storytelling experiences. As developers work, there is a wealth of customization options to tailor reports to their specific use case and draw boundless insights for end users. To ensure that Tableau gleans the best results for end users, keep these three notes in mind:
Your underlying data must be trustworthy as Tableau does little to ensure data integrity. Triple-check the numbers in your reports.
Ensure your development methods don’t significantly damage performance for both developers and end users.
Take advantage of the massive online community to uncover vital features and leverage others’ knowledge when facing challenges.
If you have any questions on Tableau or need help getting better insights from your Tableau dashboards, contact us for an analytics assessment.
