Implementing Business Intelligence Dashboards For Tesla, An Essay.

Overview of Business Intelligence Dashboards

Businesses rely on many factors for growth and success, at the helm of these factors are internal processes that are governed by the organisation itself. These internal processes should be efficient to meet the operational requirements of the enterprise and to accomplish the needs of the end consumer. A company like Tesla will depend on the quality of products it produces as well as the relationship it has with its customers to meets its business objectives. Furthermore, the organisation has grown over the past few years beyond the scope it was expected to reach, this growth has increased its data requirements including the data volumes it holds. Moreover, its recent endeavour to expand its market to include younger and middle-income customers has pushed its limits on data collection and management (Edmunds 2015).

In Tesla’s scenario collecting, analysing and interpreting data can be a tedious process due to the large volumes of data. Moreover, availability of data at the right time and place can prove to be the key to business success because of the decisions made. Nevertheless, with business intelligence dashboards Tesla’s decision-making processes can be simplified based on the data collected by the organisation. Furthermore, the system analytics can span over different departments integrating its systems to form a holistic approach to delivering services and products to the customer (Hansoti 2010).

BI dashboards are interactive web-based applications that enable users to access real-time information based on certain set standards. In essence, a company like Tesla can access customizable data on product sales and the behaviour of the customers who subscribe to their products. (Document Logistix 2017).

In general, business intelligence will track the health of an organisation or even departments based on business analytics and metrics. Moreover, prior to the implementation, the users of the dashboard should be identified to critically evaluate them. Based on Tesla’s current requirements three users are suggested; the management, the technical staff, and the executive.

Tesla operates in over 30 countries including Australia, this operational bases are coordinated by a competent management whose integral role is to assess, maintain and improve the company’s production efficiencies (Tesla 2017). Through the dashboard, the management can identify components within the organization that need adjusting hence tune them to fit their demands. Moreover, the management in this case spans all departments from marketing to the production department. This cross department implementation would integrate operations enabling a seamless execution of company’s activities (Tech advisory 2017).

Benefits of Business Intelligence Dashboards for Tesla

According to Tesla (2017) their business strategy aims to meet the demands of technology more so, through tech-product life cycles. These development stages (life cycles) require technical expertise to evaluate. A dashboard would help the technical teams keep tabs over the production processes and amend them where necessary.

Tesla has steadily matured over the years, introducing stable consumer products such as the Model S vehicle which was recently introduced. Decisions to develop such model are based on management records presented to the executive board. Therefore, incorporating the dashboard would help the executive monitor the company’s progress across its market environment (Hadley 2010).

As a result of the simple analytics that are then coupled with efficient interactive systems, BI dashboards offer a wide range of benefits to business including the overall and improved decision-making process. Tesla can benefit from similar outcomes more so when they focus on their 2016 delivery numbers that influenced the sales outcome. In general, their sales directly related to their production levels as well as their diversity (Tesla Q4 2016 2017)

BI dashboards will offer high levels of data availability that is consistent across all the organisation’s departments, which improves operational activities.

Secondly, recent studies attribute $13.1 billion to BI, this outcome is related to its analytical functionalities that will present Tesla with the necessary business assessments (Boston University 2017). 

Tesla’s performance is dependent on technological innovation, from long arm building robots to strategic forecasting tools that predict the market (Dyer & Gregersen 2015). The proposed dashboard can be customised to offer the best outcomes relating to their performance indicators i.e. innovation and milestone achievement. 

Fossil fuel reservoirs are slowly getting depleted, this gives Tesla a big market opportunity that the dashboard can exploit to help the company increase its market share.

Secondly, people are becoming more in tune with environmental conservation. The dashboards can be customised to fit environmental factors as a performance indicator improving Tesla’s contribution to environmental conservation.

Tesla’s major threat is immense competition, motor companies such as Toyota, Ford and Volkswagen still lead the park including the sales of environmental friendly vehicles (Hybrid cars 2017). The dashboard can help develop strategic plans to meet this competition through the collected data.

Accidents related to electric system such as fires. The dashboard can mitigate these outcomes through evaluation tactics of production models as well as through risk management by analysing accident’s data.

Today’s business operations are accelerated by the need of the end consumer to have faster and efficient services supported by a frequent interaction. This outcome has forced organisations to venture into real-time data sources that highlight customer needs. A BI dashboard will rely on various data sources to make accurate decisions, as shown below:

• Tesla’s annual reports i.e. Form 10-K
• Production and delivery quotas
• Prospectus supplements
• Tesla’s enterprise systems (ERP, employee and operations management systems)

• Gartner reports
• Annual governmental reports such as the Australian government reports
• Motor and manufacturing review e.g. US motor reviews and bureaus of statistics 

Data Sources for Tesla’s BI Dashboards

Tesla’s operations are determined by customers whose behaviour reflects directly on their sales. Moreover, the company operates in a highly competitive market that has been established for many years. This outcome requires accurate and quality data to offer market insights (Atkearney 2011).

Tesla’s annual reports outline the company’s production numbers including their annual and quarterly sales. In 2016 for instance, more cars were sold in the fourth quarter as compared to the first (14,820 and 22,254). Therefore, Tesla can use this number to define future productions and sales levels.

Similar approach necessitates the production and delivery numbers, however, while the annual reports focus on the general company outcomes, delivery reports define the technical aspects of business. This can help Tesla make judgment calls on production levels and employee engagement. This also applies to the ERP systems that enable Tesla to efficiently utilise its company’s resources (Njuguna 2011).

Risk assessment, legal procedures and other related proceedings are advised on Tesla’s prospectus. Through the dashboard, risks can be mitigated and avoided.

Externally, motor reviews will compare different organisations including sales and production performance. These statistics are supplemented by bureaus of statistics who collect accurate records on various companies’ variables. Tesla can use these numbers to develop strategic plans to mitigate threats, competition and develop a competitive edge.

Regardless of the data used, quality data is needed in terms of accuracy and time sensitivity. Therefore, Tesla’s dashboard should be regularly updated with the relevant data including that obtained from external sources. Technically, this can be done with active and dynamic tools that source active data.

Dashboards like any other form of business analytics are continuously growing with the increase in business operations. Now this increase in business operations has also increased the volume of data collected which makes it difficult to analyse and decipher information.

Data identification – Tesla’s first huddle will be to identify the data needed, this because the current data overload makes it difficult to identify quality information. Furthermore, even when summarised analytical tools still have to contend with the concern of data quality where the available data is filled with irrelevant and bad data.

Data integration – BI systems are never standalone projects, they depend on existing business infrastructure to meet their objectives, for instance, the data of ERP systems. Moreover, Tesla has a large clientele that is facilitated by a large organization. This produces a complex system that is not easy to integrate. Generally, this process requires heavy investments in strategic, operational and technical processes.

Implementation of a BI Dashboard for Tesla

Security and administration – When it comes to business analytics most organisations will fail at this stage. BI systems are not fix-and-forget applications, they require consistent management and adjustments based on market dynamics. Therefore, the security of the dashboard is paramount to its success and only the authorised personnel should access it. Moreover, Tesla’s dashboard should be customised to fit the demand and not the initial set up requirements (Dine 2016).

User satisfaction – Tesla has a wide range of departments filled with different people having different choices. The proposed dashboard is meant to fit into these people’s preferences, a tough and difficult endeavour.

Tesla’s operational activities places it within the maturity and stability stage of business. This stage is critical in defining the implementation of a dashboard including the deployment, experts and resources involved. An overview of the implementation process will involve the management and technical personnel. Tesla’s management will outline the requirements which will then be translated to actionable systems by the technical team.

Similar to other organization Tesla has broad and diverse requirements which will push it to seek external implementation teams. These teams will then work with internal personnel to help manage the system after deployment.

Strategic business assessment – The very first step will be to identify the business requirements including the key metrics involved such as tools, data, KPI (key performance indicators) and personnel. Moreover, it’s at this stage that parameters such as support and refreshment frequency will be identified. These parameters will help Tesla’s development team gauge the cost and time of implementation.

Data analysis – Tesla’s complied data (i.e. internal and external) as seen before will be evaluated to fit the need for BI systems. Furthermore, templates of the proposed data are made, this includes the data to be analysed by the completed system.

Architecture and modelling – Having identified the KPIs, the technical team will develop an implementation method. Fortunately, the modern market has BI management tools that provide users with the ability to choose implementation models based on their requirements such as relationships, business needs, data sources and target audience etc.

Data integration – Tesla is a well-established organization having modern day BI systems e.g. big data and analytical tools. These systems are integrated to the dashboard at this stage

Front end development – The actual system where the conceptual dashboard is created to deliver the automated system to the end user. Based on Tesla’s proposed system, a convenient user interface is developed.

Testing and deployment – Focusing on the milestones such as KPIs, Tesla deploys and tests the dashboard on the end users. Nevertheless, priority is given to the defining parameters of the dashboard where they should function as specified in the planning stage. (Dine 2016).

Several factors will determine the overall cost used to develop and implement the dashboard, these factors are:

Capabilities – dashboards can offer various functionalities i.e. visual analysis, reports and dashboarding elements. An all-inclusive system is more expensive than one that integrates several functionalities.

Vendor – Companies like InetSoft will offer dashboard models that are inclusive of the licenses which lower the cost. Therefore, based on the vendor, Tesla cost will vary.

Support and maintenance – In general, a maintenance support of 20 percent of the overall cost is needed.

Infrastructure – Cloud embedded dashboards are cheaper and offer more flexible as compared to in-house (on premise) systems (InetSoft 2017).

References

Atkearney, 2011. Better Decision Making with Proper Business Intelligence. Available at: https://www.atkearney.com/documents/10192/247903/Better_Decision_Making_with_Proper_Business_Intelligence.pdf [Accessed 27 March 2017]

Dine. S, 2016. Business Intelligence Lifecycle Management. Data source consulting. Available at: https://ds.datasourceconsulting.com/blog/business-intelligence-lifecycle-management [Accessed 27 March 2017]

Document logistix, 2017. Refreshingly simple business intelligence dashboard. Online. Available at: https://document-logistix.com/business_intelligence_dashboard.html [Accessed 27 March 2017]

Dyer.J & Gregersen. H, 2015. Decoding Tesla’s secret formula. Tech. Available at: https://www.forbes.com/sites/innovatorsdna/2015/08/19/teslas-secret-formula/#c85b8e4653c4 [Accessed 31 March 2017]

Edmunds, 2017. Tesla Expands Customer Base to Younger and Middle Class Buyers in Used Car Market, Reports. Online. Available at: https://www.edmunds.com/about/press/tesla-expands-customer-base-to-younger-and-middle-class-buyers-in-used-car-market-reports-edmundscom.html [Accessed 27 March 2017]

Hadley. J, 2010. Designing Dashboards and Scorecards for End-User Needs. Tiber solutions. Available at: https://bi-insider.com/wp-content/uploads/2010/12/Designing-Dashboards-and-Scorecards-for-End-User-Needs.pdf [Accessed 27 March 2017]

Hansoti. B, 2010. Business Intelligence Dashboard in Decision Making. Purdue University Purdue e-Pubs. Available at: https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1015&context=techdirproj [Accessed 27 March 2017]

Hybrid car, 2017. December 2016 dashboard. Available at: https://www.hybridcars.com/december-2016-dashboard/ [Accessed 31 March 2017]

InetSoft, 2017. Business intelligence solution licencing and pricing. Available at: https://www.inetsoft.com/company/bi_dashboard_pricing/ [Accessed 31 March 2017]

Juice, 2010. A Guide to Creating Dashboards People Love to Use. Available at: https://www.cpoc.org/assets/Data/guide_to_dashboard_design1.pdf [Accessed 27 March 2017]

Njuguna. J, 2011. Adoption of business intelligence dashboard and decision making in Kenya power. Available at: https://erepository.uonbi.ac.ke/bitstream/handle/11295/58755/Adoption%20of%20business%20intelligent%20dashboard%20and%20decision%20making%20at%20Kenya%20power.pdf?sequence=3 [Accessed 27 March 2017]

Tech advisory, 2015. Three types of business dashboards. Online. Available at: https://www.techadvisory.org/2015/08/three-types-of-business-dashboards/ [Accessed 27 March 2017]