Data import guide

Table of contents

  1. Importing data into BoostKPI
    1. Importing tips
  2. Data modeling tips
    1. Modeling multiple date columns
    2. Modeling column with numerical value as a dimension
    3. Modeling additional time columns as offsets.
    4. Modeling dimensions with null values.
    5. Modeling KPI with negative values.
    6. Modeling derived KPIs.
  3. After importing
    1. Add derived KPIs
    2. Mark KPIs as inverse
    3. Add descriptions and show/hide dimensions and KPIs
    4. Deleting datasets

Importing data into BoostKPI

  1. Log in to BoostKPI and select Connect along the top.
  2. Create a connection to your existing data by selecting Connections and then Add connection.
  3. Make sure to test your connection. If the connection fails or times out, ensure BoostKPI’s ip addresses are whitelisted.
  4. Create a table or view containing the data you would like to use in BoostKPI.
  5. Click the Connect data button.
    • Select your schema and table name.
    • Enter an appropriate human-readable dataset name. This can be changed later.
    • Select your dataset’s data granularity. This represents the lowest time granularity BoostKPI will use when analyzing your data and should match the level of time column aggregation in the data.
    • Click Load. This executes a query on your dataset to load column data.
    • BoostKPI will analyze the results of the query and populate a column schema.
    • For each column of the query’s results, pick date, timestamp, dimension, or KPI. KPIs should all be numeric and you should only have one date/timestamp column. Datasets in BoostKPI must have at least one dimension and at least one KPI.
    • Click Create!
  6. BoostKPI will create a new dataset and after a few seconds you will be redirected to the BoostKPI dashboard.

Importing tips

  1. (RedShift) When importing date columns from redshift, please use:

    to_char(date(original_date), ‘YYYY-MM-DD’) as date

    Redshift’s default date output format causes conflicts with our database’s expected date import format.

  2. Import the most granular data you can along your time and dimension columns. BoostKPI can then perform further aggregation either during visualization or when sending out anomaly alerts.

Data modeling tips

Modeling multiple date columns

If you have multiple dates or timestamps in the columns of your table, only set your primary time column to Date/Timestamp in the import selector and set the other time columns to Dimension.

BoostKPI uses a primary time column when displaying time series, comparing data across time, and detecting anomalies. If you wish to import multiple time columns, any time column beyond the primary one should be marked as a Dimension. It is useful to import such a time_column as an offset to the primary time column.

For example, imagine a dataset with the primary time_column as invoice_date and a payment_date column (that is after the invoice_date). The payment_date could be modeled as payment_offset ( =DATE_DIFF(payment_date, invoice_date)) in days – it is then possible to identify when there is an increase in late payments.

Modeling column with numerical value as a dimension

Consider grouping numeric dimension columns or time dimension columns into buckets when they have a large range.

BoostKPI slices and dices along dimension columns allowing you to gain insight into how KPIs have changed in subdimensions of your data. If your query includes a simple “Age” column, BoostKPI can analyze changes in KPI values based on single “Age” values. Unfortunately, if there is a change in a KPI value among 18-25 year olds, a single “Age” value breakdown will be insufficient. Instead, import a query where “Age” has been aggregated into “Age_Bucket” with buckets for important age groups, e.g., 18-24, 25-34, 35-44, 45-54, 55-64, 65+.

Depending on your use-case you might want to keep the original dimension columns around as well.

Modeling additional time columns as offsets.

Consider replacing time Dimension columns with offsets computed from the primary time column.

When a date column is included as a Dimension in BoostKPI, you will be able to see changes in your KPI broken down by this date. However if you have a conversion funnel, you might instead want to track changes in your KPIs broken down by how long people took to move from one stage of the funnel to another. For example, if you are importing purchase data with “purchase date” and “account creation date”, you could include the difference of these two columns to enable analyzing purchases based on account age at time of purchase.

It can also be useful to further bucket these offsets.

Modeling dimensions with null values.

Only import null dimension values if there is no category label that can be applied to the row. Consider replacing them with suitable String values like “none”, “missing”, etc.

BoostKPI’s analysis will omit null dimension values when breaking data down by any dimension that contains nulls. Only include null values when it makes sense for those rows to be left out when viewing a breakdown by that dimension. This would be appropriate in an instance where a dimension column only makes sense for a subset of rows as would be the case for buy/sell data: purchase_month would have a value for buy rows and null for sell rows.

Modeling KPI with negative values.

Often, negative values such as -1 are used as sentinel values to indicate that a KPI field has not been updated. In many such cases, it is better to model it as a 0 value (which can be done via a case statement in the SQL editor).

For many of its analyses, BoostKPI assumes that the KPIs are non-negative.

Modeling derived KPIs.

Do not directly import a derived KPI. Instead, import the numerator and denominator separately and then set-up the derived KPI in the BoostKPI dashboard. This will ensure that BoostKPI can correctly aggregate the derived KPI.

For example, to calculate AOV (Average Order Value), import revenue and num_orders separately. On the BoostKPI dashboard, set up AOV as revenue/num_orders.

After importing

After importing a new dataset, you can finish setting up your dataset on the dataset schema page. To navigate there, select your dataset on the BoostKPI dashboard and select the Schemas tab along the top row.

Add derived KPIs

Derived KPIs can be added to a dataset on the Schemas page by selecting the “Add derived KPI” button on the right hand side. In the menu that opens, you can either select a derived KPI to copy from an existing dataset or create a new one by dragging and dropping metric names.

Currently BoostKPI only supports derived KPIs that are a ratio (of the form X / Y) or a percentage (of the form X*100/Y).

Mark KPIs as inverse

On the dataset’s Schemas page, KPIs can be marked as “Inverse”. This indicates that an increase in the KPI should be reflected as a bad change while a decrease should be reflected as a good change. When set, an inverse KPI will use red numbers for increases and blue numbers for decreases (the opposite coloring of normal KPIs). This is useful for interpreting change when viewing cost or spend metrics that you want to minimize.

Add descriptions and show/hide dimensions and KPIs

Also on the Schemas page, you can add short descriptions for the dataset and the dataset’s dimensions. This is useful to include important dataset context that user’s may not have such as how KPIs were defined or what kind of values are stored in a dimension.

Dimensions and KPIs can also be hidden (or unhidden) to decrease clutter when performing an investigation on a dataset.

Deleting datasets

A dataset can be deleted from the BoostKPI dashboard by selecting “Edit dataset” on the right hand side of the Schemas page. At the bottom of the edit menu, type “DELETE” into the text box and then click the delete button.

Note that this only performs a soft delete of the dataset. If a hard delete of the BoostKPI metadata is required, e.g., due to legal compliance reasons, please contact