Metrics & ROI

Understanding Business Intelligence and Big Data

This year, I hand the pleasure of working in a residency/in-house consulting role for Beyondsoft. One of the many projects I worked on included an inbound marketing program designed to help senior level executives in six industries understand the use data, analytics and business intelligence (BI) in their businesses. The guide was designed to provide a 10,0oo foot view of BI and provide insight to data types and capabilities within those industry segments.

The guide also focused on removing the hype that senior level executives often encounter when they search for information on big data and data analysis. The piece helped spark a number of important conversations with key decision makers on the company’s BI division. It was supported by a full Google Adwords (search and display) and Facebook Ad Campaign, as well as a series of blog articles. Download the white paper from Slideshare:

 

How 6 Industries Use Business Intelligence To Make Strategic Decisions

Business intelligence (BI) is ubiquitous. The 2016 Business of Data Economist Intelligence Unit Report 2016 shows that 83% of enterprise-level data projects are put in place to gain a competitive edge, and 56% are using it to gain better supply chain management visibility within each operation. Here is a snapshot of how BI is used within some popular industries today:

  1. Banking: Datameer shows that the financial sector makes up 22% of all big data usage – more than any other sector. Since banks and financial institutions have data coming in from endless sources, they are constantly striving to employ the most innovative data handling systems. Three main reasons for careful management of this data are: understanding customers in order to improve satisfaction, fulfilling strict compliance requirements, and minimizing fraud and other risks. In addition, banks use data to discover new technologies to monitor and report on information.
  2. Retail: Customer relationships are critical in the retail industry, and BI can help build and maintain these relationships by providing insights on marketing segmentation, transaction handling insights, and more. According to The Economist, 64% of retailers have made financial gains in customer relationship management through their BI strategy. Through well-rounded retail analytics, owners can view historical data, manage inventory, and monitor the use of employee resources. Self-service tools can be deployed throughout the company with modules aligned to each role – from e-commerce to executives to suppliers – allowing for quick, well-informed decisions at every level.
  3. Travel: Through search analysis, travel professionals gain insight into customers’ travel intentions, providing the opportunity to tailor offers based on projected interest and demand. Through booking analysis, travel professionals can see location, route, and airline trends, allowing for market position benchmarking and competition evaluation. Travel analytics can provide insights into each segment of a traveler’s journey. These insights can aid travel agents in pinpointing new markets to enter, negotiating contracts with industry providers, and increasing conversions and acquisitions.
  4. Entertainment and Media: By utilizing data mining and algorithms, entertainment companies can accurately tailor “recommended for you” sections for each consumer on their platforms. By combining social analytics with competitive intelligence and customer data, executives can discover ways to connect with consumers, positively influencing social conversations, and increasing conversions. Journalists are getting on the bandwagon by letting data shape their stories.
  5. High Tech: As expected, the tech sector is the most likely to boost revenue through the use of data. Software companies can optimize decision-making throughout the product development process, reducing cost and risk, and lessening time to market. Predictive and by-the-minute analytics can promote efficiency and foster agility. They can use BI to stand out in an oversaturated market through differentiation and increased market awareness.
  6. Sports: BI uses in the sports realm range from enhancing performance to increasing fan engagement and satisfaction. Through performance and outcome prediction, bettors and fantasy sports players can determine which teams and players to choose. Injury analytics can help coaches and sports injury professionals decide whether or not a player should stay on the field.

How Should Companies Go about Implementing a BI Practice?

If you are new to business intelligence (BI), deciding how to implement a BI practice can seem daunting. While BI analysis implementations can be a complex discussion to have in your organization, it doesn’t have to be a complicated. The first question you have to ask yourself is how your business should go about implementing a BI strategy. Since there are many factors involved, thoughtful planning needs to take place. First assess what you currently have in your wheelhouse. The steps to do this include:

  1. Identifying existing reporting functions,
  2. Identifying technologies being used to support functions,
  3. Interviewing key people involved in running these functions,
  4. and then establishing cost, licensing, and maintenance agreements.

From here, you can then determine the goals the new program must fulfill. From there, your company can:

  1. Establish objectives,
  2. Identify roles and responsibilities,
  3. Decide on quality levels,
  4. Implement governance processes,
  5. Develop the toolset,
  6. And then develop the architecture.

Once the goals are set, it’s then time to finalize the new strategy based on key performance indicators (KPIs) that show that the goals are being met. When setting KPIs, think about what information will help executives make fast, accurate decisions relevant to overall business goals. This list should be well researched and concise. Focus on quality, not quantity. Answer the following questions before choosing KPIs:

  1. What are your clear business goals?
  2. Which performance indicators are directly linked to these business goals?
  3. Which KPIs are measurable and relate to your current growth stage?
  4. What are your leading and lagging performance indicators?
  5. Where is money being spent? Are these areas linked to goals?

Long-term targets should be set first, followed by leading and lagging indicators. Then, break them down into annual and quarterly targets and determine if short and long targets are realistic. You are then able to start to execute the plan, assign/reassign roles, update technologies, determine whether to build in-house and/or outside consultants and get stakeholder sign-off.

Always Remember: Before immersing yourself in potential BI products and services, step back to see the big picture – your strengths, weaknesses and intended achievements. Then, you can determine the optimal solutions for the situation.

How Do You Ensure Data Cleanliness?

Your last critical component of success is data cleanliness. Clean data is free of (or nearly free of) errors, including, but not limited to, incorrect spellings and punctuation, redundant data, incorrect amounts or names, and outdated data. By ensuring that data is accurate, complete, timely and valid, a company’s BI system will portray a picture that is close to reality, leading to more accurate decision making. Improper data cleansing may lead to compliance issues and information fantasy. Data cleanliness can be achieved through:

  1. Data preparation
  2. Data profiling
  3. Data standardization
  4. Geocoding
  5. Matching or linking
  6. Monitoring
  7. Updating in real time

Guidelines for data cleansing include:

  1. Recognizing that data is not 100% accurate when it is received,
  2. Acknowledging the importance of data cleansing across all platforms,
  3. Avoiding a “grab and go” method when utilizing data,
  4. Verifying accuracy and ensuring that data reflects the real world,
  5. Realizing that more data means bigger challenges to make sense of it,
  6. Expressing importance of quality to everyone who touches data,
  7. Keeping business objectives and high-level users in mind,
  8. And learning from practice: errors can happen, and the process can be long.

 

What are some of the challenges that your company faces with business intelligence? How have you started to solve them?

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