Transformational Analytics

Article by: Blob | Posted: 4th Dec 2018

4th Dec 2018

Despite much interest in business intelligence and analytics (BI&A), empirical research shows that small and medium-sized enterprises (SMEs) are still lagging behind in the proliferation of BI&A

Small and medium-sized enterprises (SMEs) account for about 90 percent of businesses and more than 50 percent of employment worldwide according to the International Finance Corporation. They play a major economic and social role, and therefore, they have become a source of economic development. Thus, the need to improve SMEs' competitiveness worldwide is crucial. However, SMEs are typically vulnerable and not robust enough to withstand the onslaught of economic and global competition. In order to survive, they must be able to monitor their business and use all their resources efficiently, especially information resources

We are living in a time where data is revolutionizing almost every aspect of human society. Businesses, for example, are now expected to use data to target their core customers down to individual addresses, and provide these customers with personalized promotions to maximize consumer spending.

However, despite all this hype around the commercial sector leading the "data revolution," for most small and medium-sized businesses (SMBs), using data feels like it's making life harder, not easier.

Via casual interaction sessions with numerous SME CEOs, we discovered that there are 5 key factors that are preventing them from converting data into actionable business insights.

This article will give you an overview of these 5 major obstacles, and will kick off a new blog series where we unpack one challenge at a time-and provide an action checklist for solving each challenge.

1. Data Interpretation

"I just want to talk to someone about how to think and approach data in my business. I want to know what questions to ask the data." - A home lifestyle product manufacturer

The most common problem we found plaguing SMBs is interpreting and making sense of the data they have in their organization. Modern technology makes it possible for all SMBs to capture data to some extent. Unfortunately, merely having the data doesn't mean they can use it to improve their business.

Here are some specific data interpretation challenges that we heard from SMBs:

  • Understanding what kind of business insights their data can provide to them
  • Understanding the value and ROI of implementing analytics tools in their organization
  • Choosing the right metrics to track
  • Placing analytics results into a business context and converting them into action items

Doing sanity checks of the analyses to make sure they are accurately answering the right questions

Can't all of these problems be solved just by hiring data analysts or consultants who know what questions to ask and how to answer them?

Yes, that may be true for larger enterprises. But the problem for SMBs is that data analysts tend to be prohibitively expensive, and leadership will often prioritize filling the "must-have" positions first-operations, sales, product managers, whoever is bringing in the revenue to keep the lights on-before they hire a data scientist.

To make things more difficult, not having a data analyst in-house means that smaller businesses have a harder time knowing how to properly make an informed decision when hiring for data analysts.

Yes, that may be true for larger enterprises. But the problem for SMBs is that data analysts tend to be prohibitively expensive, and leadership will often prioritize filling the "must-have" positions first - operations, sales, product managers, whoever is bringing in the revenue to keep the lights on - before they hire a data scientist.

To make things more difficult, not having a data analyst in-house means that smaller businesses have a harder time knowing how to properly make an informed decision when hiring for data analysts.

2. Data Collection

"Analyzing data is easy. Getting quality data is the problem because good data is either pricey or simply nonexistent." -Operations Manager

Many tools have emerged in recent years to help businesses with web analytics (Google Analytics, Mixpanel), customer relationship management (Hubspot, Salesforce), and ecommerce (Shopify, Woocommerce). Even though these tools have solved some of the more pressing data collection problems, some specific needs remain unmet.

Instead of complaining about having no data at all, most companies we interviewed face data collection challenges in a few specific areas. These areas include:

  • Collecting qualitative data about their customers
  • Converting these qualitative data into quantitative data
  • Collecting accurate data about customer behavior on their website and on their product
  • Verifying that the data collected is clean, standardized, formatted correctly, and accurate
  • Overall, data collection needs have become more sophisticated, emphasizing more on quality, rather than quantity.

3. Data Integration

"Our four different data systems do not gel well. It is a pain to gather data points from all these systems." - Operations and Marketing Specialist

Most SMBs use at least 3 SaaS tools to collect data across their businesses (e.g. Facebook Analytics + Google Analytics + Operation Database). Some companies we spoke with use up to 15 tools at the same time. Because SMBs are using more and more tools that only good at one kind of task, making these tools talk to each other becomes a daunting challenge.

In order to capture valuable information such as a customer's complete journey from awareness to revenue, companies have to pull data from all of their platforms and merge them together.

However, there are major challenges preventing SMBs from integrating their data, including:

  • Lack of structured guidelines and procedures for data management
  • Technical inability to connect various data sources together via databases or API connections
  • The lack of bandwidth to set up a logical, holistic data infrastructure
  • The huge time cost of reformatting data so they are compatible for integration

The main two barriers to solving these problems among the SMBs we spoke with are

(1) lack of management bandwidth to manage all data sources, and

(2) lack of technical human capital to construct a sound data infrastructure ahead of time.

The biggest benefit to data integration, of course, is to be able to answer bigger business questions more accurately, which brings us to the next data challenge.

4. Analytics Automation

"I have the analytics abilities to dig into my data, but valuable insights take too long to uncover." - Director of Operations

Even for companies with integrated data infrastructure and analytics expertise, data analytics can still be an extremely resource-intensive effort.

This is because data analytics tasks are extremely explorative, and analysts have to slice and dice various metrics across many different dimensions such as demographics, time, and product categories.

With each dimension added to analysis, the effort of analysis increases exponentially, with perhaps hundreds of variables needed to uncover only a few important insights. Without automation, sometimes it is physically impossible for analysts to conduct all these analyses within their time constraints.

Common automation challenges SMBs face are:

  • Lack of tools to automate tedious data cleaning and repetitive analysis processes during analysis
  • Lack of tools to efficiently advise analysts on what analyses to focus on for actionable insights
  • Lack of automated reporting mechanism post-analysis
  • The primary barrier preventing those two challenges from being resolved is the lack of analytics tools on the market that automate data cleaning and basic exploratory analysis. Without those tools, it will take even the most experienced analyst a lot of time to uncover insights, especially when you don't know where to look or where to start.

5. Analytics Adoption

"I really want to use analytics more, but I haven't done so because I need to first focus on putting out fires before thinking about optimization."

If we were to describe the previous four challenges as technical problems, the last one is very much human. To many SMB owners, even though they might know data can create value for them in some abstract way, they either don't have enough time or bandwidth to leverage their data, or they don't see the short-term benefits.

Next to data interpretation, the adoption problem is probably the most difficult one facing SMBs in their path to joining the "data revolution." Here's what was holding back the SMBs we interviewed:

  • Inability to see the immediate value of data, which means indefinitely pushing back analytics to some point in the future that will never come
  • Fear of long-term commitment to expensive analytics tools
  • Fear and frustration with the time cost of setup and implementation
  • Feeling like they could be using their time more efficiently by focusing on something more urgent, like operations or sales

For many SMBs, data analytics is a nice-to-have, not a must-have. They don't see that it is exactly because they are more constrained on time, money, and bandwidth that they need data even more than larger enterprises, because data-driven decision-making is how they can optimize their limited resources. Data is how they can make each dollar stretch further.

However, with a lack of transparent and low-commitment ways to see the value data analytics can add to their businesses, SMBs will remain stuck in outdated business practices and hesitant to fully adopt the "data revolution" of the large enterprise sector.