Category: Business Intelligence

With available data in the modern world emerging on a second-by-second basis, one of the most prominent areas of interest for organizations is the creation of continuous intelligence. A report from Gartner indicates that by 2022 more than half of all major business systems will employ continuous intelligence in their operations.

While this sounds like an exciting opportunity for any data-centric enterprise, you might wonder, though, what the pros and cons of utilizing continuous intelligence may be. Let’s break our analysis down along those lines to examine how a business might employ this emerging technology.

What Are the Pros of Using Continuous Intelligence?

By far the biggest pro of collecting and developing business intelligence continuously is greater opportunity. A company will have the opportunity to:

• Constantly monitor developments
• Stay ahead of less agile competitors
• Look at issues with the most contemporaneous data possible
• Produce analytics and work products on an on-demand basis

Consider how a bank might approach the use of continuous intelligence to handle fraud detection. As information about transactions flows in, the bank’s analysis operations would continuously stream data to machine learning algorithms. More importantly, as bad actors try to sneak around existing filters by adapting their tactics, the fraud detection system will note the tactical changes and adjust accordingly. Rather than waiting for customers to complain or relying on filters that can only be adapted sporadically, the bank will have a constantly evolving response to constantly evolving threats.

Enhanced predictability is also a major pro for continuous intelligence. A seaborne oil transportation business, for example, might use continuous monitoring of local news and social media feeds to detect upticks in pirate activity before tankers move into more dangerous regions. Decision-makers could be delivered a list of options that might include things like routing changes in response to threats, flying in additional security personnel, or notifying police and military forces in the area.

Optimization is a big opportunity for continuous intelligence. Global supply chains have come under increasing strains in recent years. Continuous intelligence systems can scan data regarding demand, supply, geopolitical activity, macroeconomic trends, and news reports to distill what the state of the market is. Rather than waiting for shelves to be empty or warehouses to be overflowing, the company could adapt in real-time to ensure goods are sufficiently meeting needs.

What Are the Cons of Using Continuous Intelligence?

The volume of data to be analyzed poses several potential hazards to organizations that depend on continuous intelligence. In particular, there’s a danger that voluminous data will create conditions where noise starts to look like a pattern. Quality control is a significant job when using continuous intelligence as a decision-making tool, and it’s critical to employ data scientists who understand the importance of always questioning the inputs and outputs.

Common human errors in handling analytics can creep in as well. With a continuous intelligence system constantly supplying insights, it’s easy to fall into the trap of treating the machine like it’s an oracle.

Additionally, dirty data is also a huge problem. If a system is feasting on continuous inputs, it can be hard to perform the necessary amount of quality control. Consider what happens if a data vendor changes the formatting of their XML documents. If the changes are implemented without notice, there’s a real risk that the continuous intelligence system will keep humming along while generating ill-informed insights.

How to Put Continuous Intelligence to Work

The adoption of continuous intelligence tools is a fact of the business world. If you want to dive into using it, it’s worth noting that you should already have a mature data operation in place. A firm that isn’t already generating numerous business intelligence insights should ease into continuous models due to the many real-time complexities involved.

Big Data capacity has to be built out in a way that exceeds what many operations currently use to produce BI. A company has to create an operating environment where bandwidth, connection speeds, and hardware are sufficient to sustain continuous data collection. For an enterprise that already has a robust Big Data department, though, continuous intelligence represents a chance to take their game to the next level.

Back to blog homepage

Having the skills needed to perform data science work is immensely beneficial in a wide range of industries and job functions. But at some point, it is also advantageous to develop a thought process that allows you to tackle problems like a data scientist. Here are 7 steps you can take to start thinking like one.

1. Understand How the Project Lifecycle Works

Every project needs to be guided through a lifecycle that goes from preparation to building and then on to finishing it. Preparation means setting goals, exploring the available data, and assessing how you’ll do the job. Building requires planning, analyzing problems, optimizing your approach, and then building viable code. Finally, finishing requires you to perform revisions, deliver the project, and wrap up loose ends. The lifecycle installs rails around the project to ensure it doesn’t suffer from mission creep.

2. Know How Time Factors into Cost-Benefit Analysis

Scraping the web for all the data you need may prove to be time-consuming, especially if the data needs to be aggressively cleaned up. On the other hand, purchasing data from a vendor can be expensive in terms of capital. There’s rarely a perfect balance between time and money so try to be receptive to which is more important on a particular project.

3. Know Why You’ve Chosen a Specific Programming Language

All programming languages have their unique strengths and weaknesses. For example, MATLAB is a very powerful language, but it often comes with licensing issues. Java handles work with a high level of precision, but it can be cumbersome. R is an excellent choice for people who need core math functions, but it can be limiting when it comes to more advanced functionality. It is essential to think about how your choice of a programming language will influence the outcome of your project.

4. Learn How to Think Outside of Your Segment of Data Science

 It’s easy to get caught in the trap of thinking certain processes are somehow more academically valid than ones aimed at the consumer market or vice versa. While something like A/B testing can feel very simple and grounded in the consumer sector, it may have applications to projects that are seemingly more technically advanced. Be open-minded in digesting information from sectors that are different from your own.

5. Appreciate Why Convincing Others is Important

Another common trap in data science is to just stay in your lane. Being a zealous advocate for your projects can make a difference in terms of getting approval and resources for them.

Develop relationships that encourage the two-way transmission of ideas and arguments. If you’re in a leadership position at a company, foster conversations with individuals who are closer to where the data gets fed into the meat grinder of analysis. Likewise, those down the ladder should be confident in presenting their ideas to people further up the chain. A good project deserves a representative who’ll advocate for it.

6. Demand Clean Data at Every Stage of a Project

Especially when there’s pressure to deliver work products, cleaning up data can sometimes feel like a secondary concern. Oftentimes, data scientists get their inputs and outputs cleaned up to a condition of “good enough” to avoid additional mundane cleaning tasks.

Data sets rarely just go away when a job is done, and that’s simply good practice for the sake of retention, auditing, and reuse. But, that also means someone else may get stuck swimming through a data swamp when they were expecting a data lake. Leave every bit of data you encounter looking cleaner than you found it.

7. Know When to Apply Critical Thinking

Data science should never be a machine that continually goes through the motions and automatically spits out results. A slew of problems can emerge when a project is too results-oriented without an eye toward critical thinking. You should always be thinking about issues like:

• Overfitting
• Correlation vs. causation
• Bayesian inference
• Getting fooled by noise
• Independent replication of results

Welcome criticism and be prepared to ask others to show how they’ve applied critical thinking to their efforts. Doing so could very well save a project from a massive misstep.

Back to blog homepage

Going to work on a big data project can leave you wondering whether your organization is handling the job as effectively as possible. It’s wise to learn from some of the most common mistakes people make on these projects. Let’s look at 5 critical big data project mistakes and how you can avoid them.

Not Knowing How to Match Tools to Tasks

It’s tempting to want to deploy the most powerful resources available. This, however, can be problematic for a host of reasons. The potential mismatch between your team members’ skills and the tools you’re asking them to use is the most critical. For example, you don’t want to have your top business analyst struggling to figure out how to modify Python code.

The goal should always be to simplify projects by providing tools that match their skills well. If a learning curve is required, you’d much prefer to have non-technical analysts trying to figure out how to use a simpler tool. For example, if the only programming language choices are between Python and R, there’s no question you want the less technically inclined folks working with R.

Failing to Emphasize Data Quality

Nothing can wreck a big data project as quickly as poor quality. The worst of possible scenarios is that low-quality and poorly structured data is fed into the system at the collection phase, ends up being used to produce analysis, and makes its way into insights and visualizations. 

There’s no such thing as being too thorough in filtering quality issues at every stage. You’ll need to keep an eye out for problems like:

• Misaligned columns and rows in sources
• Characters that were either scrubbed or altered during processing
• Out-of-date data that needs to be fetched again
• Poorly sourced data from unreliable vendors
• Data used outside of acceptable licensing terms

Data Collection without Real Analysis

It’s easy to assemble a collection of data without really putting it to work. A company can accumulate a fair amount of useful data without doing analysis, after all. For example, there is usually some value in collecting customer service data even if you never run a serious analysis on it.

 If you don’t emphasize doing analysis, delivering sights and driving decision-making, though, you’re failing to capitalize on every available ounce of value from your data. You should be looking for:

• Patterns within the data
• Ways to benefit the end customer
• Insights to provide to decision-makers
• Suggestions that can be passed along

Most companies have logs of the activities of all of the users who visit their websites. Generally, these are only utilized to deal with security and performance problems after the fact. You can, however, use weblogs to identify UX failures, SEO problems, and response rates for email and social media marketing efforts.

Not Understanding How or Why to Use Metrics

The analysis necessarily noteworthy if it’s not tied to a set of meaningful and valuable metrics. In fact, you may need to run an analysis on the data you have available just to establish what your KPIs are. Fortunately, some tools can provide confidence intervals regarding which relationships in datasets are most likely to be relevant.

For example, a company may be looking at the daily unique users for a mobile app. Unfortunately, that company might end up missing unprincipled or inaccurate activity that causes inflation in those figures. It’s important in such a situation to look at metrics that draw straight lines to meaningful performance. Even if the numbers are legit, having a bunch of unprofitable users burning through your bandwidth is not contributing to the bottom line.

Underutilizing Automation

One of the best ways to recoup some of your team’s valuable time is to automate as much of the process as possible. While the machines will always require human supervision, you don’t want to see professionals spending large amounts of time handling mundane tasks like fetching and formatting data. Fortunately, machine learning tools can be quickly trained to handle jobs like formatting collected data. If at all possible, find a way to automate the time and attention intensive phases of projects.

Back to blog homepage

What is Search-Driven Analytics? 

One of the core features of a data analytics package is usually the dashboard. This gives users access to insights and data from a variety of sources.

Unfortunately, one of the biggest challenges that come with using a dashboard is trying to sift through the available information. This can be especially problematic at businesses that are generating hundreds of insights on a daily basis. So how exactly does someone get right to what they need without sifting through numerous reports or going down an alphabetized list?

What is Search-Driven Analytics Used For?

An answer that’s growing in popularity is search. This is not search in the sense that you’re familiar with from Google, although it operates much in the same way. Instead, we’re talking about a search-driven system that allows users to type in everyday sentences to get results from your data. 

For example, someone might go into the dashboard and type in “How many customer service calls did we take in 2019?” The backend for the dashboard will understand the query and return a list of results that are ranked from most relevant to least. Within seconds, the user can click on the appropriate analysis product and find the information they need.

How Does Search-Driven Analytics Work?

The engine that drives most search-driven technology is natural language processing (NLP). A technology that has been around since the 1950s, NLP has become vastly more powerful in the last decade due to increases in parallel processing in CPUs and GPUs.

An NLP system is usually designed to take a corpus of words to help a machine understand the basic ideas that underpin each bit of a dataset. When a user types in a search, the NLP algorithm will compare the search query against the scores it has for each dataset. Those datasets that look the most likely to match are then returned as a result.

Especially in settings where organizations have narrow interests, NLP can be very powerful and precise. A logistics firm, for example, might have an NLP setup that can answer questions like:

• “Where are all current shipments from Asia?”
• “How long does the average delivery take?”
• “What were the company’s fuel costs for January?”

These are extremely natural questions for a user to want answered, and the search-driven dashboard can address them with ease in the vast majority of cases.

The Role of Data Visualizations

Another benefit of this approach is that data is usually fed straight into the dashboard in the form of visualizations. If you enter a query to see how many widgets were sold each month, you can click on the right item and a graph of widget sales will appear on the right-hand side. There’s no need to run an analysis engine or load anything into Excel to get a viable work product. It simply appears in a matter of seconds.

Why Use This Approach?

The beauty of a search-driven system is that it can help users browse through data, create ad-hoc reports and make decisions on the fly. If someone needs to pull up the total for all store inventories of a handful of items within a retail chain, for example, they can type that into the search bar and get an answer in seconds. They can then see the data, produce any necessary reports or paperwork and move forward with their task of refilling inventory levels.

Notably, this approach makes it much easier for less tech-savvy individuals to follow the data. In turn, that frees software engineers and data scientists within your organization to focus on building more robust systems, working with data and fixing advanced problems.

Conclusion

In the modern data-driven culture, a lot is made of onboarding people. Just 5 or 10 years ago, that often meant excluding those who lacked the technical expertise to function in a data-driven business setting. 

The increasing ease of access to data due to tools like search-driven analytics makes it possible to bring more people on board. Likewise, it allows users to get their answers quickly rather than trying to navigate through complex interfaces. Search-driven analytics allows organizations to be more efficient and effective in leveraging the data they have access to.

Customer relationship management (CRM) systems are widely used in many businesses and organizations today. While it’s great to have all your customer information compiled and accessible in one source, you may not be maximizing the value of your CRM. In particular, big data, business intelligence (BI) and analytics packages offer an opportunity to take your customer data to the next level. Let’s take a look at how you can achieve this with your customer data.

How to Leverage CRM as a Source for Analysis

Most CRM systems operate on top of databases that have the necessary capabilities to feed data into analytics and BI systems. While skilled database programmers can get a lot of mileage out of writing complex queries, the reality is that most of the data can simply be pulled into the big data pipeline by way of database connectors. These are small components of applications that are used to talk with databases in languages like MySQL, MongoDB, Fox Pro, and MSSQL.

Once you’ve pulled the data into your analysis engine, a host of functions can be used to study it. For example, a company might use the information from their CRM to:

• Perform a time-series analysis of customer performance
• Analyze which appeals from email marketing content seem to drive the greatest returns
• Determine which customers are at risk of moving on to competitors
• Find appeals that reinforce customer loyalty
• Spot customer service failures
• Analyze social media postings by customers to assess their experience with the company

What Methods Are Used?

Suppose your business wants to determine which email campaign appeals are worth reusing. Working from copies of email content, you can conduct a word cloud analysis that shows which concepts were strongly featured. Response data from the CRM can then be used to identify which words and phrases have performed best. 

These items can then be organized into a BI dashboard widget that tells those writing the emails how to structure their content. For example, a market research firm might find that case studies drive more users into the marketing funnel than news about the practice. Marketers can then write new campaign emails based on that provided data. Almost as important, they can also access email performance and refine their approach until the material is exemplary.

 Tools that are ideal for projects like this include:

• Sentiment analysis
• Marketing data
• Pattern recognition
• Word cloud analysis

Such analysis will also require a constant stream of data going from the CRM into the analytics engine and onward to the BI dashboards. Done right, this sort of big data program can convert something you’re already accumulating, such as customer relationship data, into insights that drive decision-making at all levels.

How Much Data is There?

Data for analysis can come from a slew of sources, and it’s important to have a CRM that allows you to access as many potential data sources as possible. For example, a company shouldn’t draw the line at collecting email addresses. You can also ask customers to include their social media accounts, such as handles from Twitter, LinkedIn, and Instagram.

Server logs should also be mined for interesting data points. You can, for example, study IP addresses and user logins to determine where a prospective customer might be in the marketing funnel. If you see that a lot of leads are dropping out at a certain stage, such as after signing up to receive your email newsletter, you can then start to analyze what’s misfiring at this stage in the process. 

Once the problem is corrected, you can even use the CRM data to identify which customers you should reconnect with or retarget. You might, for example, send an offer for discounted goods or services to increase your customer lifetime value.

Conclusion

At many businesses, the CRM system is a highly underutilized resource. By coupling it with big data and an effective BI package, you can quickly turn it into a sales-driving machine. Team members will be excited to see the new marketing and sales tools at their disposal, and customers will value the increased engagement.